The Use of Features from Fluorescence, Thermography, and NDVI Imaging to Detect Biotic Stress in Lettuce

Active vs. Passive Thermal Imaging for Helping the Early Detection of Soil-Borne Rot Diseases on Wild Rocket [Diplotaxis tenuifolia (L.) D.C.]

Cultivation of wild rocket [Diplotaxis tenuifolia (L.) D.C.] as a baby-leaf vegetable for the high-convenience food chain is constantly growing due to its nutritional and taste qualities. As is well known, these crops are particularly exposed to soil-borne fungal diseases and need to be effectively protected. At present, wild rocket disease management is performed by using permitted synthetic fungicides or through the application of agro-ecological and biological methods that must be optimized. In this regard, the implementation of innovative digital-based technologies, such as infrared thermography (IT), as supporting systems to decision-making processes is welcome. In this work, leaves belonging to wild rocket plants inoculated with the soil-borne pathogens Rhizoctonia solani Kühn and Sclerotinia sclerotiorum (Lib.) de Bary were analyzed and monitored by both active and passive thermographic methods and compared with visual detection. A comparison between the thermal analysis carried out in both medium (MWIR)- and long (LWIR)-wave infrared was made and discussed. The results achieved highlight how the monitoring based on the use of IT is promising for carrying out an early detection of the rot diseases induced by the investigated pathogens, allowing their detection in 3-6 days before the canopy is completely wilted. Active thermal imaging has the potential to detect early soil-borne rotting diseases.

Multi-omics assisted breeding for biotic stress resistance in soybean

Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker-trait associations involved in regulating biotic stress responses in soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We suggest using 'integrated omics' to precisely understand how soybean responds to different biotic stresses. We also discuss the potential challenges of integrating multi-omics for the functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.

Detecting Infected Cucumber Plants with Close-Range Multispectral Imagery

This study used close-range multispectral imagery over cucumber plants inside a commercial greenhouse to detect powdery mildew due to Podosphaera xanthii. It was collected using a MicaSense® RedEdge camera at 1.5 m over the top of the plant. Image registration was performed using Speeded-Up Robust Features (SURF) with an affine geometric transformation. The image background was removed using a binary mask created with the aligned NIR band of each image, and the illumination was corrected using Cheng et al.’s algorithm. Different features were computed, including RGB, image reflectance values, and several vegetation indices. For each feature, a fine Gaussian Support Vector Machines algorithm was trained and validated to classify healthy and infected pixels. The data set to train and validate the SVM was composed of 1000 healthy and 1000 infected pixels, split 70–30% into training and validation datasets, respectively. The overall validation accuracy was 89, 73, 82, 51, and 48%, respectively, for blue, green, red, red-edge, and NIR band image. With the RGB images, we obtained an overall validation accuracy of 89%, while the best vegetation index image was the PMVI-2 image which produced an overall accuracy of 81%. Using the five bands together, overall accuracy dropped from 99% in the training to 57% in the validation dataset. While the results of this work are promising, further research should be considered to increase the number of images to achieve better training and validation datasets.

Molecular Mapping of Water-Stress Responsive Genomic Loci in Lettuce (Lactuca spp.) Using Kinetics Chlorophyll Fluorescence, Hyperspectral Imaging and Machine Learning

Deep understanding of genetic architecture of water-stress tolerance is critical for efficient and optimal development of water-stress tolerant cultivars, which is the most economical and environmentally sound approach to maintain lettuce production with limited irrigation. Lettuce (Lactuca sativa L.) production in areas with limited precipitation relies heavily on the use of ground water for irrigation. Lettuce plants are highly susceptible to water-stress, which also affects their nutrient uptake efficiency. Water stressed plants show reduced growth, lower biomass, and early bolting and flowering resulting in bitter flavors. Traditional phenotyping methods to evaluate water-stress are labor intensive, time-consuming and prone to errors. High throughput phenotyping platforms using kinetic chlorophyll fluorescence and hyperspectral imaging can effectively attain physiological traits related to photosynthesis and secondary metabolites that can enhance breeding efficiency for water-stress tolerance. Kinetic chlorophyll fluorescence and hyperspectral imaging along with traditional horticultural traits identified genomic loci affected by water-stress. Supervised machine learning models were evaluated for their accuracy to distinguish water-stressed plants and to identify the most important water-stress related parameters in lettuce. Random Forest (RF) had classification accuracy of 89.7% using kinetic chlorophyll fluorescence parameters and Neural Network (NN) had classification accuracy of 89.8% using hyperspectral imaging derived vegetation indices. The top ten chlorophyll fluorescence parameters and vegetation indices selected by sequential forward selection by RF and NN were genetically mapped using a L. sativa × L. serriola interspecific recombinant inbred line (RIL) population. A total of 25 quantitative trait loci (QTL) segregating for water-stress related horticultural traits, 26 QTL for the chlorophyll fluorescence traits and 34 QTL for spectral vegetation indices (VI) were identified. The percent phenotypic variation (PV) explained by the horticultural QTL ranged from 6.41 to 19.5%, PV explained by chlorophyll fluorescence QTL ranged from 6.93 to 13.26% while the PV explained by the VI QTL ranged from 7.2 to 17.19%. Eight QTL clusters harboring co-localized QTL for horticultural traits, chlorophyll fluorescence parameters and VI were identified on six lettuce chromosomes. Molecular markers linked to the mapped QTL clusters can be targeted for marker-assisted selection to develop water-stress tolerant lettuce.

Thermal Imaging for Plant Stress Detection and Phenotyping

In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.

Phenomics-based GWAS analysis reveals the genetic architecture for drought resistance in cotton

Drought resistance (DR) is a complex trait that is regulated by a variety of genes. Without comprehensive profiling of DR-related traits, the knowledge of the genetic architecture for DR in cotton remains limited. Thus, there is a need to bridge the gap between genomics and phenomics. In this study, an automatic phenotyping platform (APP) was systematically applied to examine 119 image-based digital traits (i-traits) during drought stress at the seedling stage, across a natural population of 200 representative upland cotton accessions. Some novel i-traits, as well as some traditional i-traits, were used to evaluate the DR in cotton. The phenomics data allowed us to identify 390 genetic loci by genome-wide association study (GWAS) using 56 morphological and 63 texture i-traits. DR-related genes, including GhRD2, GhNAC4, GhHAT22 and GhDREB2, were identified as candidate genes by some digital traits. Further analysis of candidate genes showed that Gh_A04G0377 and Gh_A04G0378 functioned as negative regulators for cotton drought response. Based on the combined digital phenotyping, GWAS analysis and transcriptome data, we conclude that the phenomics dataset provides an excellent resource to characterize key genetic loci with an unprecedented resolution which can inform future genome-based breeding for improved DR in cotton.

Blurred lines: integrating emerging technologies to advance plant biosecurity

Plant diseases threaten global food security and biodiversity. Rapid dispersal of pathogens particularly via human means has accelerated in recent years. Timely detection of plant pathogens is essential to limit their spread. At the same time, international regulations must keep abreast of advances in plant disease diagnostics. In this review we describe recent progress in developing modern plant disease diagnostics based on detection of pathogen components, high-throughput image analysis, remote sensing, and machine learning. We discuss how different diagnostic approaches can be integrated in detection frameworks that can work at different scales and account for sampling biases. Lastly, we briefly discuss the requirements to apply these advances under regulatory settings to improve biosecurity measures globally.

Novel Remote Sensing Index of Electron Transport Rate Predicts Primary Production and Crop Health in L. sativa and Z. mays

Photosynthesis performance can be assessed quantitatively with light response curves. These curves record the Electron Transport Rate (ETR) as a function of light intensity. Then, statistical fit on these curves parameterize light use efficiency, maximum photosynthetic activity and the reaction of the apparatus to stress. While this technique is performed with portable fluorometers in field conditions, it is difficult to scale it to the canopy level. The Fraunhofer line discrimination technique, which detects fluorescence signals emitted during photosynthesis, is a promising method to assess photosynthetic performance of canopies. In this study, we define a remote sensing ETR index based on a combination of three parameters: sun-induced fluorescence, normalized differential vegetation index and light intensity. Two representatives of C3 and C4 photosynthesis, L. sativa and Z. mays, experienced a fertilization concentrations gradient. ETR increased with light intensity in both crops. In L. sativa, ETR assumed a linear relationship between the photosynthetic activity and light intensity, with a correlation of R2 = 0.99 to the portable fluorometer. Additional parametrization revealed a resilience of its reaction centers to photoinhibition in maximum light intensities. When Z. mays experienced open field conditions, ETR correlated with the plant’s status. While the results of this study are promising, the index still requires validation in terms of temporal track and spatial variability.

UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning

A remote sensing technique was developed to detect citrus canker in laboratory conditions and was verified in the grove by utilizing an unmanned aerial vehicle (UAV). In the laboratory, a hyperspectral (400–1000 nm) imaging system was utilized for the detection of citrus canker in several disease development stages (i.e., asymptomatic, early, and late symptoms) on Sugar Belle leaves and immature (green) fruit by using two classification methods: (i) radial basis function (RBF) and (ii) K nearest neighbor (KNN). The same imaging system mounted on an UAV was used to detect citrus canker on tree canopies in the orchard. The overall classification accuracy of the RBF was higher (94%, 96%, and 100%) than the KNN method (94%, 95%, and 96%) for detecting canker in leaves. Among the 31 studied vegetation indices, the water index (WI) and the Modified Chlorophyll Absorption in Reflectance Index (ARI and TCARI 1) more accurately detected canker in laboratory and in orchard conditions, respectively. Immature fruit was not a reliable tissue for early detection of canker. However, the proposed technique successfully distinguished the late stage canker-infected fruit with 92% classification accuracy. The UAV-based technique achieved 100% classification accuracy for identifying healthy and canker-infected trees.