High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging

Applications of optical sensing and imaging spectroscopy in indoor farming: A systematic review

As demand for food continues to rise, innovative methods are needed to sustainably and efficiently meet the growing pressure on agriculture. Indoor farming and controlled environment agriculture have emerged as promising approaches to address this challenge. However, optimizing fertilizer usage, ensuring homogeneous production, and reducing agro-waste remain substantial challenges in these production systems. One potential solution is the use of optical sensing technology, which can provide real-time data to help growers make informed decisions and enhance their operations. optical sensing can be used to analyze plant tissues, evaluate crop quality and yield, measure nutrients, and assess plant responses to stress. This paper presents a systematic literature review of the current state of using spectral-optical sensors and hyperspectral imaging for indoor farming, following the PRISMA 2020 guidelines. The study surveyed existing studies from 2017 to 2023 to identify gaps in knowledge, provide researchers and farmers with current trends, and offer recommendations and inspirations for possible new research directions. The results of this review will contribute to the development of sustainable and efficient methods of food production.

Spectroscopy-based chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients

Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant's nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over more extensive areas like field- or landscape scale. Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-based remote sensing or spectroscopy has been explored in the current study to predict the foliar nutritional status of the cashew crop. Novel spectral indices (normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near-infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the full dataset) datasets. An independent validation dataset was used for the validation of the algorithms tested. The approach of spectral indices yielded very poor and unreliable predictions for all eleven nutrients. Among the chemometric models tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yielded acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist for nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine regression for calcium, magnesium, iron, copper, and boron; elastic net for sulphur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micro-nutrients. The developed approach is suggested to employ within the operational workflows for site-specific and precision nutrient management of the cashew orchards.

Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning

Hyperaccumulators are the material basis and key to the phytoremediation of heavy metal contaminated soils. Conventional methods for screening hyperaccumulators are highly dependent on the time- and labor-consuming sampling and chemical analysis. In this study, a novel spectral approach assisted with multi-task deep learning was proposed to streamline accumulating ecotype screening, heavy metal stress discrimination, and heavy metals quantification in plants. The significant Cd/Zn co-hyperaccumulator Sedum alfredii and its non-accumulating ecotype were stressed by Cd, Zn, and Pb. Spectral images of leaves were rapidly acquired by hyperspectral imaging. The self-designed deep learning architecture was composed of a shallow network (ENet) for accumulating ecotype identification, and a multi-task network (HMNet) for heavy metal stress type and accumulation prediction simultaneously. To further assess the robustness of the networks, they were compared with conventional machine learning models (i.e., partial least squares (PLS) and support vector machine (SVM)) on a series of evaluation metrics of classification, multi-label classification, and regression. S. alfredii with heavy metals accumulation capability was identified by ENet with 100 % accuracy. HMNet reduced overfitting and outperformed machine learning models with the average exact match ratio (EMR) of heavy metal stress discrimination increased by 7.46 %, and residual prediction deviations (RPD) of heavy metal concentrations prediction increased by 53.59 %. The method succeeded in rapidly and accurately discriminating heavy metal stress with EMRs over 91 % and accuracies over 96 %, and in predicting heavy metals accumulation with an average RPD of 3.29 for Zn, 2.57 for Cd, and 2.53 for Pb, indicating the satisfactory practicability and potential for sensing heavy metals accumulation. This study provides a relatively novel spectral method to facilitate hyperaccumulator screening and heavy metals accumulation prediction in the phytoremediation process.

Attenuated total reflection Fourier-transform infrared spectroscopy reveals environment specific phenotypes in clonal Japanese knotweed

BACKGROUND

Japanese knotweed (Reynoutria japonica var. japonica), a problematic invasive species, has a wide geographical distribution. We have previously shown the potential for attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometrics to segregate regional differentiation between Japanese knotweed plants. However, the contribution of environment to spectral differences remains unclear. Herein, the response of Japanese knotweed to varied environmental habitats has been studied. Eight unique growth environments were created by manipulation of the red: far-red light ratio (R: FR), water availability, nitrogen, and micronutrients. Their impacts on plant growth, photosynthetic parameters, and ATR-FTIR spectral profiles, were explored using chemometric techniques, including principal component analysis (PCA), linear discriminant analysis, support vector machines (SVM) and partial least squares regression. Key wavenumbers responsible for spectral differences were identified with PCA loadings, and molecular biomarkers were assigned. Partial least squared regression (PLSR) of spectral absorbance and root water potential (RWP) data was used to create a predictive model for RWP.

RESULTS

Spectra from plants grown in different environments were differentiated using ATR-FTIR spectroscopy coupled with SVM. Biomarkers highlighted through PCA loadings corresponded to several molecules, most commonly cell wall carbohydrates, suggesting that these wavenumbers could be consistent indicators of plant stress across species. R: FR most affected the ATR-FTIR spectra of intact dried leaf material. PLSR prediction of root water potential achieved an R2 of 0.8, supporting the potential use of ATR-FTIR spectrometers as sensors for prediction of plant physiological parameters.

CONCLUSIONS

Japanese knotweed exhibits environmentally induced phenotypes, indicated by measurable differences in their ATR-FTIR spectra. This high environmental plasticity reflected by key biomolecular changes may contribute to its success as an invasive species. Light quality (R: FR) appears critical in defining the growth and spectral response to environment. Cross-species conservation of biomarkers suggest that they could function as indicators of plant-environment interactions including abiotic stress responses and plant health.

Up-regulation of non-photochemical quenching improves water use efficiency and reduces whole-plant water consumption under drought in Nicotiana tabacum

Water supply limitations will likely impose increasing restrictions on future crop production, underlining a need for crops that use less water per mass of yield. Water use efficiency (WUE) therefore becomes a key consideration in developing resilient and productive crops. In this study, we hypothesized that it is possible to improve WUE under drought conditions via modulation of chloroplast signals for stomatal opening by up-regulation of non-photochemical quenching (NPQ). Nicotiana tabacum plants with strong overexpression of the PsbS gene encoding PHOTOSYSTEM II SUBUNIT S, a key protein in NPQ, were grown under differing levels of drought. The PsbS-overexpressing lines lost 11% less water per unit CO2 fixed under drought and this did not have a significant effect on plant size. Depending on growth conditions, the PsbS-overexpressing lines consumed from 4-30% less water at the whole-plant level than the corresponding wild type. Leaf water and chlorophyll contents showed a positive relation with the level of NPQ. This study therefore provides proof of concept that up-regulation of NPQ can increase WUE, and as such is an important step towards future engineering of crops with improved performance under drought.

Non-Destructive Monitoring of Foliar Fungicide Efficacy with Hyperspectral Sensing in Grapevine

Frequent fungicide applications are required to manage grapevine powdery mildew (Erysiphe necator). However, this practice is costly and has led to widespread fungicide resistance. A method of monitoring in-field fungicide efficacy could help growers maximize spray-interval length, thereby reducing costs and the rate of fungicide resistance emergence. The goal of this study was to evaluate if hyperspectral sensing in the visible to shortwave infrared range (400 to 2,400 nm) can quantify foliar fungicide efficacy on grape leaves. Commercial formulations of metrafenone, Bacillus mycoides isolate J (BmJ), and sulfur were applied on Chardonnay grapevines in vineyard or greenhouse settings. Foliar reflectance was measured with handheld hyperspectral spectroradiometers at multiple days post-application. Fungicide efficacy was estimated as a proxy for fungicide residue and disease control measured with the Blackbird microscopy imaging robot. Treatments could be differentiated from the untreated control with an accuracy of 73.06% for metrafenone, 67.76% for BmJ, and 94.10% for sulfur. The change in spectral reflectance was moderately correlated with the cube root of the area under the disease progress curve for metrafenone- and sulfur-treated samples (R2 = 0.38 and 0.36, respectively) and with sulfur residue (R2 = 0.42). BmJ treatment impacted foliar physiology by enhancing the leaf mass/area and reducing the nitrogen and total phenolic content as estimated from spectral reflectance. The results suggest that hyperspectral sensing can be used to monitor in-situ fungicide efficacy, and the prediction accuracy depends on the fungicide and the time point measured. The ability to monitor in-situ fungicide efficacy could facilitate more strategic fungicide applications and promote sustainable grapevine protection. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

A Multi-Target Regression Method to Predict Element Concentrations in Tomato Leaves Using Hyperspectral Imaging

Recent years have seen the development of novel, rapid, and inexpensive techniques for collecting plant data to monitor the nutritional status of crops. These techniques include hyperspectral imaging, which has been widely used in combination with machine learning models to predict element concentrations in plants. When there are multiple elements, the machine learning models are trained with spectral features to predict individual element concentrations; this type of single-target prediction is known as single-target regression. Although this method can achieve reliable accuracy for some elements, there are others that remain less accurate. We aimed to improve the accuracy of element concentration predictions by using a multi-target regression method that sequentially augmented the original input features (hyperspectral imaging) by chaining the predicted element concentration values. To evaluate the multi-target method, the concentrations of 17 elements in tomato leaves were predicted and compared with the single-target regression results. We trained 5 machine learning models with hyperspectral data and predicted element concentration values and found a significant improvement in the prediction accuracy for 10 elements (Mg, P, S, Mn, Fe, Co, Cu, Sr, Mo, and Cd). Furthermore, our multi-target regression method outperformed single-target predictions by increasing the coefficient of determination (R2) for elements such as Mn, Cu, Co, Fe, and Mg by 12.5%, 10.3%, 11%, 10%, and 8.4%, respectively. Hence, our multi-target method can improve the accuracy of predicting 10-element concentrations compared to single-target regression.

Remote Sensing Techniques: Hyperspectral Imaging and Data Analysis

Hyperspectral imaging is a remote sensing technique that enables remote, noninvasive measurement of plant traits. Here, we outline the procedures for camera setup, scanning, and calibration, along with the acquisition of black and white reference materials, which are the key steps in collecting hyperspectral imagery. We also discuss the development of predictive models such as partial least-squares regression, using both large and small datasets, which are used to predict plant traits from hyperspectral data. To ensure practical applicability, we provide code examples that allow readers to immediately implement these techniques in real-world scenarios. We introduce these topics to beginners in an accessible and understandable manner.

Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields

Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400-1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R2P) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R2P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R2P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.

Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.

Quantifying physiological trait variation with automated hyperspectral imaging in rice

Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice (Oryza sativa). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88) and carbon to nitrogen ratio (C:N, n=88) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R 2 = 0.797 and RMSEP = 0.264 for N; R 2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies.

Prediction and visualization of gene modulated ultralow cadmium accumulation in brown rice grains by hyperspectral imaging

Monitoring (including prediction and visualization) the gene modulated cadmium (Cd) accumulation in rice grains is one of the most important steps for identification of key transporter genes responsible for grain Cd accumulation and breeding low grain-Cd-accumulating rice cultivars. A method to predict and visualize the gene modulated ultralow Cd accumulation in brown rice grains based on the hyperspectral image (HSI) technology is proposed in this study. Firstly, the Vis-NIR HSIs of brown rice grain samples with 48Cd content levels induced by gene modulation (ranging from 0.0637 to 0.1845 mg/kg) are collected using HSI system. Then, Kernel-ridge (KRR) and random forest (RFR) regression models based on full spectral data and the data after feature dimension reduction (FDR) with kernel principal component analysis (KPCA) and truncated singular value decomposition (TSVD) algorithms are established to predict the Cd contents. RFR model shows poor performance due to the over-fitting based on the full spectral data, while the KRR model can obtain a good predict accuracy with R_p² of 0.9035, RMSEP of 0.0037 and RPD of 3.278. After the FDR of the full spectral data, the RFR model combined with TSVD reaches the optimum prediction accuracy with R_p² of 0.9056, RMSEP of 0.0074 and RPD of 3.318, and the best prediction precision of KRR model can also be further enhanced by TSVD with R_p² of 0.9224, RMSEP of 0.0067 and RPD of 3.512. Finally, the visualization of the predicted Cd accumulation in brown rice grains are realized based on the best regression model (KRR + TSVD). The results of this work indicate that Vis-NIR HSI has great potential for detection and visualization gene modulation induced ultralow Cd accumulation and transport in rice crops.

Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging

One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.

A leaf-level spectral library to support high throughput plant phenotyping: Predictive accuracy and model transfer

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive; and models show poor transferability among different datasets. This study had three specific objectives: (i) assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum, (ii) evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), and (iii) investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (average R 2 0.688), with Partial Least Squares Regression outperforming Deep Neural Network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (average R 2 0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (average R 2 0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping; whereas extra-weight spiking improves model transferability and extends its utility.

PhenoWell®-A novel screening system for soil-grown plants

As agricultural production is reaching its limits regarding outputs and land use, the need to further improve crop yield is greater than ever. The limited translatability from in vitro lab results into more natural growth conditions in soil remains problematic. Although considerable progress has been made in developing soil-growth assays to tackle this bottleneck, the majority of these assays use pots or whole trays, making them not only space- and resource-intensive, but also hampering the individual treatment of plants. Therefore, we developed a flexible and compact screening system named PhenoWell® in which individual seedlings are grown in wells filled with soil allowing single-plant treatments. The system makes use of an automated image-analysis pipeline that extracts multiple growth parameters from individual seedlings over time, including projected rosette area, relative growth rate, compactness, and stockiness. Macronutrient, hormone, salt, osmotic, and drought stress treatments were tested in the PhenoWell® system. The system is also optimized for maize with results that are consistent with Arabidopsis while different in amplitude. We conclude that the PhenoWell® system enables a high-throughput, precise, and uniform application of a small amount of solution to individually soil-grown plants, which increases the replicability and reduces variability and compound usage.

Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels

Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum. In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography-Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R² of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills.

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R² and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion

Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson's correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses.

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces as a Predictor of Macadamia Crop Nutrition

Tree crop yield is highly dependent on fertiliser inputs, which are often guided by the assessment of foliar nutrient levels. Traditional methods for nutrient analysis are time-consuming but hyperspectral imaging has potential for rapid nutrient assessment. Hyperspectral imaging has generally been performed using the adaxial surface of leaves although the predictive performance of spectral data has rarely been compared between adaxial and abaxial surfaces of tree leaves. We aimed to evaluate the capacity of laboratory-based hyperspectral imaging (400-1000 nm wavelengths) to predict the nutrient concentrations in macadamia leaves. We also aimed to compare the prediction accuracy from adaxial and abaxial leaf surfaces. We sampled leaves from 30 macadamia trees at 0, 6, 10 and 26 weeks after flowering and captured hyperspectral images of their adaxial and abaxial surfaces. Partial least squares regression (PLSR) models were developed to predict foliar nutrient concentrations. Coefficients of determination (R²_P) and ratios of prediction to deviation (RPDs) were used to evaluate prediction accuracy. The models reliably predicted foliar nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), copper (Cu), manganese (Mn), sulphur (S) and zinc (Zn) concentrations. The best-fit models generally predicted nutrient concentrations from spectral data of the adaxial surface (e.g., N: R²_P = 0.55, RPD = 1.52; P: R²_P = 0.77, RPD = 2.11; K: R²_P = 0.77, RPD = 2.12; Ca: R²_P = 0.75, RPD = 2.04). Hyperspectral imaging showed great potential for predicting nutrient status. Rapid nutrient assessment through hyperspectral imaging could aid growers to increase orchard productivity by managing fertiliser inputs in a more-timely fashion.

Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery

Phycobilisomes and chlorophyll-a (Chla) play important roles in the photosynthetic physiology of red macroalgae and serve as the primary light-harvesting antennae and reaction center for photosystem II. Neopyropia is an economically important red macroalga widely cultivated in East Asian countries. The contents and ratios of 3 main phycobiliproteins and Chla are visible traits to evaluate its commercial quality. The traditional analytical methods used for measuring these components have several limitations. Therefore, a high-throughput, nondestructive, optical method based on hyperspectral imaging technology was developed for phenotyping the pigments phycoerythrin (PE), phycocyanin (PC), allophycocyanin (APC), and Chla in Neopyropia thalli in this study. The average spectra from the region of interest were collected at wavelengths ranging from 400 to 1000 nm using a hyperspectral camera. Following different preprocessing methods, 2 machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish the best prediction models for PE, PC, APC, and Chla contents. The prediction results showed that the PLSR model performed the best for PE (R _Test ² = 0.96, MAPE = 8.31%, RPD = 5.21) and the SVR model performed the best for PC (R _Test ² = 0.94, MAPE = 7.18%, RPD = 4.16) and APC (R _Test ² = 0.84, MAPE = 18.25%, RPD = 2.53). Two models (PLSR and SVR) performed almost the same for Chla (PLSR: R _Test ² = 0.92, MAPE = 12.77%, RPD = 3.61; SVR: R _Test ² = 0.93, MAPE = 13.51%, RPD =3.60). Further validation of the optimal models was performed using field-collected samples, and the result demonstrated satisfactory robustness and accuracy. The distribution of PE, PC, APC, and Chla contents within a thallus was visualized according to the optimal prediction models. The results showed that hyperspectral imaging technology was effective for fast, accurate, and noninvasive phenotyping of the PE, PC, APC, and Chla contents of Neopyropia in situ. This could benefit the efficiency of macroalgae breeding, phenomics research, and other related applications.

Rapid online plant leaf area change detection with high-throughput plant image data

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.

Estimation of amino acid contents in maize leaves based on hyperspectral imaging

Estimation of the amino acid content in maize leaves is helpful for improving maize yield estimation and nitrogen use efficiency. Hyperspectral imaging can be used to obtain the physiological and biochemical parameters of maize leaves with the advantages of being rapid, non-destructive, and high throughput. This study aims to estimate the multiple amino acid contents in maize leaves using hyperspectral imaging data. Two nitrogen (N) fertilizer experiments were carried out to obtain the hyperspectral images of fresh maize leaves. The partial least squares regression (PLSR) method was used to build the estimation models of various amino acid contents by using the reflectance of all bands, sensitive band range, and sensitive bands. The models were then validated with the independent dataset. The results showed that (1) the spectral reflectance of most amino acids was more sensitive in the range of 400-717.08 nm than other bands. The estimation accuracy was better by using the reflectance of the sensitive band range than that of all bands; (2) the sensitive bands of most amino acids were in the ranges of 505.39-605 nm and 651-714 nm; and (3) among the 24 amino acids, the estimation models of the β-aminobutyric acid, ornithine, citrulline, methionine, and histidine achieved higher accuracy than those of other amino acids, with the R ², relative root mean square error (RE), and relative percent deviation (RPD) of the measured and estimated value of testing samples in the range of 0.84-0.96, 8.79%-19.77%, and 2.58-5.18, respectively. This study can provide a non-destructive and rapid diagnostic method for genetic sensitive analysis and variety improvement of maize.

Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features

Potassium (K) is one of the most important elements influencing cotton metabolism, quality, and yield. Due to the characteristics of strong fluidity and fast redistribution of the K in plants, it leads to rapid transformation of the K lack or abundance in plant leaves; therefore, rapid and accurate estimation of potassium content in leaves (LKC, %) is a necessary prerequisite to solve the regulation of plant potassium. In this study, we concentrated on the LKC of cotton in different growth stages, an estimation model based on the combined characteristics of wavelet decomposition spectra and image was proposed, and discussed the potential of different combined features in accurate estimation of the LKC. We collected hyperspectral imaging data of 60 main-stem leaves at the budding, flowering, and boll setting stages of cotton, respectively. The original spectrum (R) is decomposed by continuous wavelet transform (CWT). The competitive adaptive reweighted sampling (CARS) and random frog (RF) algorithms combined with partial least squares regression (PLSR) model were used to determine the optimal decomposition scale and characteristic wavelengths at three growth stages. Based on the best "CWT spectra" model, the grayscale image databases were constructed, and the image features were extracted by using color moment and gray level co-occurrence matrix (GLCM). The results showed that the best decomposition scales of the three growth stages were CWT-1, 3, and 9. The best growth stage for estimating LKC in cotton was the boll setting stage, with the feature combination of "CWT-9 spectra + texture," and its determination coefficients (R ²val) and root mean squared error (RMSEval) values were 0.90 and 0.20. Compared with the single R model (R ²val = 0.66, RMSEval = 0.34), the R ²val increased by 0.24. Different from our hypothesis, the combined feature based on "CWT spectra + color + texture" cannot significantly improve the estimation accuracy of the model, it means that the performance of the estimation model established with more feature information is not correspondingly better. Moreover, the texture features contributed more to the improvement of model performance than color features did. These results provide a reference for rapid and non-destructive monitoring of the LKC in cotton.

New strategies on the application of artificial intelligence in the field of phytoremediation

Artificial Intelligence (AI) is expected to play a crucial role in the field of phytoremediation and its effective management in monitoring the growth of the plant in different contaminated soils and their phenotype characteristic such as the biomass of plants. This review focuses on recent applications of various AI techniques and remote sensing approaches in the field of phytoremediation to monitor plant growth with relevant morphological parameters using novel sensors, cameras, and associated modern technologies. Novel sensing and various measurement techniques are highlighted. Input parameters are used to develop futuristic models utilizing AI and statistical approaches. Additionally, a brief discussion has been presented on the use of AI techniques to detect metal hyperaccumulation in all parts of the plant, carbon capture, and sequestration along with its effect on food production to ensure food safety and security. This article highlights the application, limitation, and future perspectives of phytoremediation in monitoring the mobility, bioavailability, seasonal variation, effect of temperature on plant growth, and plant response to the heavy metals in soil by using the AI technique. Suggestions are made for future research in this area to analyze which would help to enhance plant growth and improve food security in long run.

Applicability of hyperspectral imaging during salinity stress in rice for tracking Na+ and K+ levels in planta

The ratio of Na+ and K+ is an important determinant of the magnitude of Na+ toxicity and osmotic stress in plant cells. Traditional analytical approaches involve destructive tissue sampling and chemical analysis, where real-time observation of spatio-temporal experiments across genetic or breeding populations is unrealistic. Such an approach can also be very inaccurate and prone to erroneous biological interpretation. Analysis by Hyperspectral Imaging (HSI) is an emerging non-destructive alternative for tracking plant nutrient status in a time-course with higher accuracy and reduced cost for chemical analysis. In this study, the feasibility and predictive power of HSI-based approach for spatio-temporal tracking of Na+ and K+ levels in tissue samples was explored using a panel recombinant inbred line (RIL) of rice (Oryza sativa L.; salt-sensitive IR29 x salt-tolerant Pokkali) with differential activities of the Na+ exclusion mechanism conferred by the SalTol QTL. In this panel of RILs the spectrum of salinity tolerance was represented by FL499 (super-sensitive), FL454 (sensitive), FL478 (tolerant), and FL510 (super-tolerant). Whole-plant image processing pipeline was optimized to generate HSI spectra during salinity stress at EC = 9 dS m-1. Spectral data was used to create models for Na+ and K+ prediction by partial least squares regression (PLSR). Three datasets, i.e., mean image pixel spectra, smoothened version of mean image pixel spectra, and wavelength bands, with wide differences in intensity between control and salinity facilitated the prediction models with high R2. The smoothened and filtered datasets showed significant improvements over the mean image pixel dataset. However, model prediction was not fully consistent with the empirical data. While the outcome of modeling-based prediction showed a great potential for improving the throughput capacity for salinity stress phenotyping, additional technical refinements including tissue-specific measurements is necessary to maximize the accuracy of prediction models.

Capturing crop adaptation to abiotic stress using image-based technologies

Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

BACKGROUND

Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features.

RESULTS

The models with a single color feature from RGB images predicted chlorophyll content with R² ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R² ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R² of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R² of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy.

CONCLUSION

All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum.

Transcriptome and hyperspectral profiling allows assessment of phosphorus nutrient status in rice under field conditions

Phosphorus (P) is one of the macronutrients indispensable for crop production, and therefore it is important to understand the potential of plants to adapt to low P conditions. We compared growth and leaf genome-wide transcriptome of four rice cultivars during growth between two fields with different amount of available phosphate and further analysed the acceptable range of P levels for normal growth from the view of both appearance traits and internal P nutrient status, which was measured by profiling the expression of the P indicator gene. This demonstrated that rice plants have a robustness to moderate P-deficient conditions expressing a system for P acquisition and usage without any effects on yield potential and that P indicator gene expression could be a useful index for early diagnosis of P status in plants. To develop a simple method for assessment of P status, we tried to predict the expression level using reflectance spectroscopy and hyperspectral imaging, thereby providing models with good performance. Our findings suggest that rice plants have the potential to adapt to moderate low P conditions in the field and showed that the hyperspectral technique is one of the useful tools for simple measurement of molecular-level dynamics reflecting internal nutrient conditions.

Digital plant pathology: a foundation and guide to modern agriculture

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host-pathogen-environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

Applications of hyperspectral imaging in plant phenotyping

Our ability to interrogate and manipulate the genome far exceeds our capacity to measure the effects of genetic changes on plant traits. Much effort has been made recently by the plant science research community to address this imbalance. The responses of plants to environmental conditions can now be defined using a variety of imaging approaches. Hyperspectral imaging (HSI) has emerged as a promising approach to measure traits using a wide range of wavebands simultaneously in 3D to capture information in lab, glasshouse, or field settings. HSI has been applied to define abiotic, biotic, and quality traits for optimisation of crop management.

Condensed tannins as antioxidants that protect poplar against oxidative stress from drought and UV-B

Condensed tannins (CTs, proanthocyanidins) are widespread polymeric flavan-3-ols known for their ability to bind proteins. In poplar (Populus spp.), leaf condensed tannins are induced by both biotic and abiotic stresses, suggesting diverse biological functions. Here we demonstrate the ability of CTs to function as physiological antioxidants, preventing oxidative and cellular damage in response to drought and UV-B irradiation. Chlorophyll fluorescence was used to monitor photosystem II performance, and both hydrogen peroxide and malondialdehyde content was assayed as a measure of oxidative damage. Transgenic MYB-overexpressing poplar (Populus tremula × P. tremuloides) with high CT content showed reduced photosystem damage and lower hydrogen peroxide and malondialdehyde content after drought and UV-B stress. This antioxidant effect of CT was observed using two different poplar MYB CT regulators, in multiple independent lines and different genetic backgrounds. Additionally, low-CT MYB134-RNAi transgenic poplars showed enhanced susceptibility to drought-induced oxidative stress. UV-B radiation had different impacts than drought on chlorophyll fluorescence, but all high-CT poplar lines displayed reduced sensitivity to both stresses. Our data indicate that CTs are significant defences against oxidative stress. The broad distribution of CTs in forest systems that are exposed to diverse abiotic stresses suggests that these compounds have wider functional roles than previously realized.

Dynamics and genetic regulation of leaf nutrient concentration in barley based on hyperspectral imaging and machine learning

Biofortification, the enrichment of nutrients in crop plants, is of increasing importance to improve human health. The wild barley nested association mapping (NAM) population HEB-25 was developed to improve agronomic traits including nutrient concentration. Here, we evaluated the potential of high-throughput hyperspectral imaging in HEB-25 to predict leaf concentration of 15 mineral nutrients, sampled from two field experiments and four developmental stages. Particularly accurate predictions were obtained by partial least squares regression (PLS) modeling of leaf concentrations for N, P and K reaching coefficients of determination of 0.90, 0.75 and 0.89, respectively. We recognized nutrient-specific patterns of variation of leaf nutrient concentration between developmental stages. A number of quantitative trait loci (QTL) associated with the simultaneous expression of leaf nutrients were detected, indicating their potential co-regulation in barley. For example, the wild barley allele of QTL-4H-1 simultaneously increased leaf concentration of N, P, K and Cu. Similar effects of the same QTL were previously reported for nutrient concentrations in grains, supporting a potential parallel regulation of N, P, K and Cu in leaves and grains of HEB-25. Our study provides a new approach for nutrient assessment in large-scale field experiments to ultimately select genes and genotypes supporting plant biofortification.

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants' disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.

Detection of apple proliferation disease in Malus × domestica by near infrared reflectance analysis of leaves

In this study near infrared spectroscopical analysis of dried and ground leaves was performed and combined with a multivariate data analysis to distinguish 'Candidatus Phytoplasma mali' infected from non-infected apple trees (Malus × domestica). The bacterium is the causative agent of Apple Proliferation, one of the most threatening diseases in commercial apple growing regions. In a two-year study, leaves were sampled from three apple orchards, at different sampling events throughout the vegetation period. The spectral data were analyzed with a principal component analysis and classification models were developed. The model performance for the differentiation of Apple Proliferation diseased from non-infected trees increased throughout the vegetation period and gained best results in autumn. Even with asymptomatic leaves from infected trees a correct classification was possible indicating that the spectral-based method provides reliable results even if samples without visible symptoms are analyzed. The wavelength regions that contributed to the differentiation of infected and non-infected trees could be mainly assigned to a reduction of carbohydrates and N-containing organic compounds. Wet chemical analyses confirmed that N-containing compounds are reduced in leaves from infected trees. The results of our study provide a valuable indication that spectral analysis is a promising technique for Apple Proliferation detection in future smart farming approaches.

Chemometric approaches for calibrating high-throughput spectral imaging setups to support digital plant phenotyping by calibrating and transferring spectral models from a point spectrometer

Visible and near-infrared (Vis-NIR) spectral imaging is appearing as a potential tool to support high-throughput digital agricultural plant phenotyping. One of the uses of spectral imaging is to predict non-destructively the chemical constituents in the plants such as nitrogen content which can be related to the functional status of plants. However, before using high-throughput spectral imaging, it requires extensive calibration, just as needed for any other spectral sensor. Calibrating the high-throughput spectral imaging setup can be a challenging task as the resources needed to run experiments in high-throughput setups are far more than performing measurements with point spectrometers. Hence, to supply a resource-efficient approach to calibrate spectral cameras integrated with high-throughput plant phenotyping setups, this study proposes the use of chemometric calibration transfer (CT) and model update. The main idea was to use a point spectrometer to develop the primary model and transfer it to the spectral cameras integrated into the high-throughput setups. The potential of the approach was showed using a real Vis-NIR dataset related to nitrogen prediction in wheat plants measured with point spectrometer, tabletop spectral cameras and spectral cameras integrated with a high-throughput plant phenotyping setup. For CT and model update, direct standardization and parameter-free calibration enhancement approaches were explored. A key aim of this study was to only use and compare techniques that does not require any further optimization as they can be easily implemented by the plant biologist in future applications. The proposed approach based on the transfer of point spectroscopy models to spectral cameras in a high-throughput setup can allow spectral calibrations to be sharable and widely applicable, thus helping the global digital plant phenotyping community.

The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat

Crown rot disease is caused by Fusarium pseudograminearum and is one of the major stubble-soil fungal diseases threatening the cereal industry globally. It causes failure of grain establishment, which brings significant yield loss. Screening crops affected by crown rot is one of the key tools to manage crown rot, because it is necessary to understand disease infection conditions, identify the severity of infection, and discover potential resistant varieties. However, screening crown rot is challenging as there are no clear visible symptoms on leaves at early growth stages. Hyperspectral imaging (HSI) technologies have been successfully used to better understand plant health and disease incidence, including light absorption rate, water and nutrient distribution, and disease classification. This suggests HSI imaging technologies may be used to detect crown rot at early growing stages, however, related studies are limited. This paper briefly describes the symptoms of crown rot disease and traditional screening methods with their limitations. It, then, reviews state-of-art imaging technologies for disease detection, from color imaging to hyperspectral imaging. In particular, this paper highlights the suitability of hyperspectral-based screening methods for crown rot disease. A hypothesis is presented that HSI can detect crown-rot-infected plants before clearly visible symptoms on leaves by sensing the changes of photosynthesis, water, and nutrients contents of plants. In addition, it describes our initial experiment to support the hypothesis and further research directions are described.

Molecular and functional insights into glutathione S-transferase genes associated with salt stress in Halothece sp. PCC7418

Evolution and function of glutathione S-transferase (GST) in primordial oxygenic phototrophs such as cyanobacteria are poorly understood. In this study, we identified and functionally characterized the GST gene family in the halotolerant cyanobacterium Halothece sp. PCC7418. Four putative Halothece-GSTs had very low homology, which implies evolutionary divergence. Of these, H0647, H0729 and H3557 were differentially expressed by oxidative stress whereas H3557 was highly and specifically upregulated under salt stress. In vitro analysis revealed that the recombinant H3557 exhibited GST activity toward 1-chloro-2, 4-dinitrobenzene (CDNB) and glutathione (GSH). H3557 displayed a broad range of activity at pH 6.5-10.5. Kinetic parameters showed the apparent K_m for CDNB and GSH was 0.14 and 0.75 mM, respectively. H3557 remained catalytically active in the presence of NaCl. Structural modelling supported that H3557 is salt-adaptive enzyme with highly acidic residues on the protein surface. The vital function of H3557 in heterologous expression system was evaluated. The H3557-expressing cells were more tolerant to H₂ O₂ -induced oxidative stress compared with other GST-expressing cells and conferred salt tolerance. Taken together, the findings of this study provide insights into the molecular and cellular functions of GST in cyanobacteria, particularly under salt stress, which is less understood compared with other species.

Including leaf trait information helps empirical estimation of jmax from vcmax in cool-temperate deciduous forests

Understanding the uncertainty in the parameterization of the two photosynthetic capacity parameters, leaf maximum carboxylation rate (Vcmax), and maximum electron transport rate (Jmax), is crucial for modeling and predicting carbon fluxes in terrestrial ecosystems. In gas exchange models, to date, Jmax is typically estimated from Vcmax based on a linear regression. However, recent studies have revealed that this relationship varies, dependent upon species, leaf groups, and time, so it is doubtful that the regression applies universally. Furthermore, far less is known regarding how other leaf traits affect the regression. In this study we analyzed the two key photosynthetic parameters and popularly measurable leaf traits, leaf chlorophyll concentration and leaf mass per area (LMA), of cool-temperate forest stands in Japan, aiming to construct a simple regression applicable to temperate deciduous forests, at least. The analysis was based on a long-term field dataset covering years of data for both sunlit and shaded leaves at different altitudes. Results showed that the best-fitted slope of the regression differed markedly from those previously reported, which were typically acquired from sunlit leaves. LMA had a significant effect on the regression, producing the lowest root mean square errors and the highest ratio of performance to deviation values (RPD = 2.017). Although more data are needed to validate in other ecosystems, our approach at least provides a promising way to substantially improve photosynthesis model predictions, by introducing leaf traits into the popular empirical regression of Jmax against Vcmax, and ultimately to better understand the functioning of the photosynthetic machinery.

Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging

The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images to identify healthy and individual nutrient deficiencies of grapevine leaves. Features such as mean reflectance, mean first derivative reflectance, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD) were employed at various stages in the ultraviolet (UV), visible (VIS) and near-infrared (N.I.R.) regions for our experiment. Leaves were examined visually in the laboratory and grouped as either healthy (i.e. control) or unhealthy. Then, the features of the leaves were extracted from these two groups. In a second experiment, features of individual nutrient-deficient leaves (e.g., N, K and Mg) were also analysed and compared with those of control leaves. Furthermore, a customised support vector machine (SVM) was used to demonstrate that these features can be utilised with a high degree of effectiveness to identify unhealthy samples and not only to distinguish from control and nutrient deficient but also to identify individual nutrient defects. Therefore, the proposed work corroborated that HS imaging has excellent potential to analyse features based on healthiness and individual nutrient deficiencies of grapevine leaves.

High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax ginseng Meyer) Using a Hyperspectral Reflectance Image

Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants’ growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.

Integration of Visible and Thermal Imagery with an Artificial Neural Network Approach for Robust Forecasting of Canopy Water Content in Rice

A total of 120 rice plant samples were scanned by visible and thermal proximal sensing systems under different water stress levels to evaluate the canopy water content (CWC). The oven-drying method was employed for assessing the canopy’s water state. This CWC is of great importance for irrigation management decisions. The proposed framework is to integrate visible and thermal imaging data using an artificial neural network as a valuable promising implement for accurately estimating the water content of the plant. The RGB-based features included 20 color vegetation indices (VI) and 6 gray level co-occurrence matrix-based texture features (GLCMF). The thermal imaging features were two thermal indicators (T), namely normalized relative canopy temperature (NRCT) and the crop water stress index (CWSI), that were deliberated by plant temperatures. These features were applied with a back-propagation neural network (BPNN) for training the samples with minimal loss on a cross-validation set. Model behavior was affected by filtering high-level features and optimizing hyperparameters of the model. The results indicated that feature-based modeling from both visible and thermal images achieved better performance than features from the individual visible or thermal image. The supreme prediction variables were 21 features: 14VI, 5GLCMF, and 2T. The fusion of color–texture–thermal features greatly improved the precision of water content evaluation (99.40%). Its determination coefficient (R2 = 0.983) was the most satisfied with an RMSE of 0.599. Overall, the methodology of this work can support decision makers and water managers to take effective and timely actions and achieve agricultural water sustainability.

Protective role of fat hen (Chenopodium album L.) extract and gamma irradiation treatments against fusarium root rot disease in sunflower plants

One of the most drastic diseases causing economic losses in sunflower crops is fusarium root rot caused by Fusarium solani. Plant extracts and ionizing radiation provide alternative environmentally safe control agents that have a significant role in controlling and overcoming this fungal plant pathogen. In the present study, the effect of different concentrations of aqueous Chenopodium album extract (2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5 and 6.0%) and gamma radiation at a dose of 6 Gy were examined for their efficacy in inducing resistance of sunflower plants against fusarium root rot caused by F. solani MG-3 by evaluation of some physiological and biochemical parameters of infected and healthy plants under greenhouse conditions. The pre-treatment of sunflower seeds with 6% C. album extract and 6 Gy gamma radiation reduced fusarium incidence from 47.49% to 28.25%. Also, nucleic acid content, ascorbic acid, α-tocopherol, anthocyanin, total flavonoids, proline, glycine betaine and lipid components significantly increased in irradiated infected plants treated with C. album extract, while H₂ O₂ content and lipid peroxidation markedly decreased as compared with healthy control plants. Moreover, treatment with gamma radiation reduced the amount of unsaturated fatty acids through accumulation of saturated fatty acids compared with non-irradiated plants; treatment with C. album extract also enhanced the content of unsaturated fatty acids, with a noticeable decrease in saturated fatty acid content. Hence, C. album extract and gamma radiation can be used to enhance biological control of fusarium root rot of sunflower plants.

Breeding for Climate Change Resilience: A Case Study of Loblolly Pine (Pinus taeda L.) in North America

Earth's atmosphere is warming and the effects of climate change are becoming evident. A key observation is that both the average levels and the variability of temperature and precipitation are changing. Information and data from new technologies are developing in parallel to provide multidisciplinary opportunities to address and overcome the consequences of these changes in forest ecosystems. Changes in temperature and water availability impose multidimensional environmental constraints that trigger changes from the molecular to the forest stand level. These can represent a threat for the normal development of the tree from early seedling recruitment to adulthood both through direct mortality, and by increasing susceptibility to pathogens, insect attack, and fire damage. This review summarizes the strengths and shortcomings of previous work in the areas of genetic variation related to cold and drought stress in forest species with particular emphasis on loblolly pine (Pinus taeda L.), the most-planted tree species in North America. We describe and discuss the implementation of management and breeding strategies to increase resilience and adaptation, and discuss how new technologies in the areas of engineering and genomics are shaping the future of phenotype-genotype studies. Lessons learned from the study of species important in intensively-managed forest ecosystems may also prove to be of value in helping less-intensively managed forest ecosystems adapt to climate change, thereby increasing the sustainability and resilience of forestlands for the future.

Modeling of Diurnal Changing Patterns in Airborne Crop Remote Sensing Images

Airborne remote sensing technologies have been widely applied in field crop phenotyping. However, the quality of current remote sensing data suffers from significant diurnal variances. The severity of the diurnal issue has been reported in various plant phenotyping studies over the last four decades, but there are limited studies on the modeling of the diurnal changing patterns that allow people to precisely predict the level of diurnal impacts. In order to comprehensively investigate the diurnal variability, it is necessary to collect time series field images with very high sampling frequencies, which has been difficult. In 2019, Purdue agricultural (Ag) engineers deployed their first field visible to near infrared (VNIR) hyperspectral gantry platform, which is capable of repetitively imaging the same field plots every 2.5 min. A total of 8631 hyperspectral images of the same field were collected for two genotypes of corn plants from the vegetative stage V4 to the reproductive stage R1 in the 2019 growing season. The analysis of these images showed that although the diurnal variability is very significant for almost all the image-derived phenotyping features, the diurnal changes follow stable patterns. This makes it possible to predict the imaging drifts by modeling the changing patterns. This paper reports detailed diurnal changing patterns for several selected plant phenotyping features such as Normalized Difference Vegetation Index (NDVI), Relative Water Content (RWC), and single spectrum bands. For example, NDVI showed a repeatable V-shaped diurnal pattern, which linearly drops by 0.012 per hour before the highest sun angle and increases thereafter by 0.010 per hour. The different diurnal changing patterns in different nitrogen stress treatments, genotypes and leaf stages were also compared and discussed. With the modeling results of this work, Ag remote sensing users will be able to more precisely estimate the deviation/change of crop feature predictions caused by the specific imaging time of the day. This will help people to more confidently decide on the acceptable imaging time window during a day. It can also be used to calibrate/compensate the remote sensing result against the time effect.

Root System Phenotying of Soil-Grown Plants via RGB and Hyperspectral Imaging

Phenotyping root systems provide essential information for plant breeding, particularly aiming for better abiotic stress resistance. Rhizobox systems provide a field-near growth environment for in situ imaging of root systems in soil. A protocol for RGB and hyperspectral imaging of rhizobox-grown plants is presented that enables gathering of root structural (morphology, architecture) as well as functional (water content, decomposition) information. The protocol exemplifies the setup of a root phenotyping platform combining low-cost RGB with advanced short-wave infrared hyperspectral imaging. For both types of imaging approach, the essential steps of an image analysis pipeline are provided to retrieve biological information on breeding-relevant traits from the imaging datasets.

Pepper Plants Leaf Spectral Reflectance Changes as a Result of Root Rot Damage

Symptoms of root stress are hard to detect using non-invasive tools. This study reveals proof of concept for vegetation indices’ ability, usually used to sense canopy status, to detect root stress, and performance status. Pepper plants were grown under controlled greenhouse conditions under different potassium and salinity treatments. The plants’ spectral reflectance was measured on the last day of the experiment when more than half of the plants were already naturally infected by root disease. Vegetation indices were calculated for testing the capability to distinguish between healthy and root-damaged plants using spectral measurements. While no visible symptoms were observed in the leaves, the vegetation indices and red-edge position showed clear differences between the healthy and the root-infected plants. These results were achieved after a growth period of 32 days, indicating the ability to monitor root damage at an early growing stage using leaf spectral reflectance.

Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology

Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution.

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.