Combining Hyperspectral Reflectance Indices and Multivariate Analysis to Estimate Different Units of Chlorophyll Content of Spring Wheat under Salinity Conditions

High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images

Chlorophyll content reflects plants' photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared. The most sensitive spectral (SVIs) and color vegetation indices (CVIs) for chlorophyll content were extracted and evaluated as reliable estimators of chlorophyll content. Using an ASD FieldSpec 4 Hi-Res spectroradiometer and a portable red, green, and blue (RGB) camera, 3600 hyperspectral reflectance measurements and 800 RGB images were acquired from lettuce grown across a gradient of nutrient levels. Ground measurements of leaf chlorophyll were acquired using an SPAD-502 m calibrated via laboratory chemical analyses. The results revealed a strong relationship between chlorophyll content and SPAD-502 readings, with an R2 of 0.95 and a correlation coefficient (r) of 0.975. The developed AutoML models outperformed all traditional models, yielding the highest values of the coefficient of determination in prediction (Rp2) for all vegetation indices (VIs). The combination of SVIs and CVIs achieved the best prediction accuracy with the highest Rp2 values ranging from 0.89 to 0.98, respectively. This study demonstrated the feasibility of spectral and color vegetation indices as estimators of chlorophyll content. Furthermore, the developed AutoML models can be integrated into embedded devices to control nutrient cycles in aquaponics systems.

Multi-index fuzzy comprehensive evaluation model with information entropy of alfalfa salt tolerance based on LiDAR data and hyperspectral image data

Rapid, non-destructive and automated salt tolerance evaluation is particularly important for screening salt-tolerant germplasm of alfalfa. Traditional evaluation of salt tolerance is mostly based on phenotypic traits obtained by some broken ways, which is time-consuming and difficult to meet the needs of large-scale breeding screening. Therefore, this paper proposed a non-contact and non-destructive multi-index fuzzy comprehensive evaluation model for evaluating the salt tolerance of alfalfa from Light Detection and Ranging data (LiDAR) and HyperSpectral Image data (HSI). Firstly, the structural traits related to growth status were extracted from the LiDAR data of alfalfa, and the spectral traits representing the physical and chemical characteristics were extracted from HSI data. In this paper, these phenotypic traits obtained automatically by computation were called Computing Phenotypic Traits (CPT). Subsequently, the multi-index fuzzy evaluation system of alfalfa salt tolerance was constructed by CPT, and according to the fuzzy mathematics theory, a multi-index Fuzzy Comprehensive Evaluation model with information Entropy of alfalfa salt tolerance (FCE-E) was proposed, which comprehensively evaluated the salt tolerance of alfalfa from the aspects of growth structure, physiology and biochemistry. Finally, comparative experiments showed that: (1) The multi-index FCE-E model based on the CPT was proposed in this paper, which could find more salt-sensitive information than the evaluation method based on the measured Typical Phenotypic Traits (TPT) such as fresh weight, dry weight, water content and chlorophyll. The two evaluation results had 66.67% consistent results, indicating that the multi-index FCE-E model integrates more information about alfalfa and more comprehensive evaluation. (2) On the basis of the CPT, the results of the multi-index FCE-E method were basically consistent with those of Principal Component Analysis (PCA), indicating that the multi-index FCE-E model could accurately evaluate the salt tolerance of alfalfa. Three highly salt-tolerant alfalfa varieties and two highly salt-susceptible alfalfa varieties were screened by the multi-index FCE-E method. The multi-index FCE-E method provides a new method for non-contact non-destructive evaluation of salt tolerance of alfalfa.

Melatonin Role in Plant Growth and Physiology under Abiotic Stress

Phyto-melatonin improves crop yield by mitigating the negative effects of abiotic stresses on plant growth. Numerous studies are currently being conducted to investigate the significant performance of melatonin in crops in regulating agricultural growth and productivity. However, a comprehensive review of the pivotal performance of phyto-melatonin in regulating plant morpho-physiological and biochemical activities under abiotic stresses needs to be clarified. This review focused on the research on morpho-physiological activities, plant growth regulation, redox status, and signal transduction in plants under abiotic stresses. Furthermore, it also highlighted the role of phyto-melatonin in plant defense systems and as biostimulants under abiotic stress conditions. The study revealed that phyto-melatonin enhances some leaf senescence proteins, and that protein further interacts with the plant's photosynthesis activity, macromolecules, and changes in redox and response to abiotic stress. Our goal is to thoroughly evaluate phyto-melatonin performance under abiotic stress, which will help us better understand the mechanism by which phyto-melatonin regulates crop growth and yield.

Opportunity and challenges of phenotyping plant salt tolerance

Salinity is a key factor limiting agricultural production worldwide. Recent advances in field phenotyping have enabled the recording of the environmental history and dynamic response of plants by considering both genotype × environment (G×E) interactions and envirotyping. However, only a few studies have focused on plant salt tolerance phenotyping. Therefore, we analyzed the potential opportunities and major challenges in improving plant salt tolerance using advanced field phenotyping technologies. RGB imaging and spectral and thermal sensors are the most useful and important sensing techniques for assessing key morphological and physiological traits of plant salt tolerance. However, field phenotyping faces challenges owing to its practical applications and high costs, limiting its use in early generation breeding and in developing countries.

Comparative analysis of physiological variations and genetic architecture for cold stress response in soybean germplasm

Soybean (Glycine max L.) is susceptible to low temperatures. Increasing lines of evidence indicate that abiotic stress-responsive genes are involved in plant low-temperature stress response. However, the involvement of photosynthesis, antioxidants and metabolites genes in low temperature response is largely unexplored in Soybean. In the current study, a genetic panel of diverse soybean varieties was analyzed for photosynthesis, chlorophyll fluorescence and leaf injury parameters under cold stress and control conditions. This helps us to identify cold tolerant (V100) and cold sensitive (V45) varieties. The V100 variety outperformed for antioxidant enzymes activities and relative expression of photosynthesis (Glyma.08G204800.1, Glyma.12G232000.1), GmSOD (GmSOD01, GmSOD08), GmPOD (GmPOD29, GmPOD47), trehalose (GmTPS01, GmTPS13) and cold marker genes (DREB1E, DREB1D, SCOF1) than V45 under cold stress. Upon cold stress, the V100 variety showed reduced accumulation of H₂O₂ and MDA levels and subsequently showed lower leaf injury compared to V45. Together, our results uncovered new avenues for identifying cold tolerant soybean varieties from a large panel. Additionally, we identified the role of antioxidants, osmo-protectants and their posttranscriptional regulators miRNAs such as miR319, miR394, miR397, and miR398 in Soybean cold stress tolerance.

Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer

Leaf chlorophyll content is used as a major indicator of plant stress and growth, and hyperspectral remote sensing is frequently used to monitor the chlorophyll content. Hyperspectral reflectance has been used to evaluate vegetation properties such as pigment content, plant structure and physiological features using portable spectroradiometers. However, the prices of these devices have not yet decreased to consumer-affordable levels, which prevents widespread use. In this study, a system based on a cost-effective fingertip-sized spectrometer (Colorcompass-LF, a total price for the proposed solution was approximately 1600 USD) was evaluated for its ability to estimate the chlorophyll contents of radish and wasabi leaves and was compared with the Analytical Spectral Devices FieldSpec4. The chlorophyll contents per leaf area (cm2) of radish were generally higher than those of wasabi and ranged from 42.20 to 94.39 μg/cm2 and 11.39 to 40.40 μg/cm2 for radish and wasabi, respectively. The chlorophyll content was estimated using regression models based on a one-dimensional convolutional neural network (1D-CNN) that was generated after the original reflectance from the spectrometer measurements was de-noised. The results from an independent validation dataset confirmed the good performance of the Colorcompass-LF after spectral correction using a second-degree polynomial, and very similar estimation accuracies were obtained for the measurements from the FieldSpec4. The coefficients of determination of the regression models based on 1D-CNN were almost same (with R2 = 0.94) and the ratios of performance to deviation based on reflectance after spectral correction using a second-degree polynomial for the Colorcompass-LF and the FieldSpec4 were 4.31 and 4.33, respectively.