Spectral reflectance pattern in soybean for assessing yellow mosaic disease

Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models

Rice is the staple food of more than half of the population of the world and India as well. One of the major constraints in rice production is frequent occurrence of pests and diseases and one of them is rice blast which often causes yield loss varying from 10 to 30%. Conventional approaches for disease assessment are time-consuming, expensive, and not real-time; alternately, sensor-based approach is rapid, non-invasive and can be scaled up in large areas with minimum time and effort.  In the present study, hyperspectral remote sensing for the characterization and severity assessment of rice blast disease was exploited. Field experiments were conducted with 20 genotypes of rice having sensitive and resistant cultivars grown under upland and lowland conditions at Almora, Uttarakhand, India. The severity of the rice blast was graded from 0 to 9 in accordance to International Rice Research Institute (IRRI).  Spectral observations in field were taken using a hand-held portable spectroradiometer in range of 350-2500 nm followed by spectral discrimination of different disease severity levels using Jeffires-Matusita (J-M) distance. Then, evaluation of 26 existing spectral indices (r≥0.8) was done corresponding to blast severity levels and linear regression prediction models were also developed. Further, the proposed ratio blast index (RBI) and normalized difference blast index (NDBI) were developed using all possible combinations of their correlations with severity level followed by their quantification to identify the best indices. Thereafter, multivariate models like support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) were also used to estimate blast severity. Jeffires-Matusita distance was separating almost all severity levels having values >1.92 except levels 4 and 5. The 26 prediction models were effective at predicting blast severity with R2 values from 0.48 to 0.85. The best developed spectral indices for rice blast were RBI (R1148, R1301) and NDBI (R1148, R1301) with R2 of 0.85 and 0.86, respectively. Among multivariate models, SVM was the best model with calibration R2=0.99; validation R2=0.94, RMSE=0.7, and RPD=4.10. The methodology developed paves way for early detection and large-scale monitoring and mapping using satellite remote sensors at farmers' fields for developing better disease management options.

Normalized Difference Vegetation Index (NDVI) for soybean biomass and nutrient uptake estimation in response to production systems and fertilization strategies

The system fertilization approach emerged to improve nutrient use efficiency in croplands. This new fertilization concept aims at taking advantage of nutrient cycling within an agroecosystem to obtain maximum production from each nutrient unit. To monitor this effect, methodologies such as the Normalized Difference Vegetation Index (NDVI) are promising to evaluate plant biomass and nutrient content. We evaluated the use of NDVI as a predictor of shoot biomass, P and K uptake, and yield in soybean. Treatments consisted of two production systems [integrated crop-livestock system (ICLS) and cropping system (CS)] and two periods of phosphorus (P) and potassium (K) fertilization (crop fertilization—P and K applied at soybean sowing—and system fertilization—P and K applied in the pasture establishment). NDVI was evaluated weekly from the growth stage V2 up to growth stage R8, using the Greenseeker® canopy sensor. At the growth stages V4, V6, R2, and R4, plants were sampled after NDVI evaluation for chemical analysis. Soybean yield and K uptake were similar between production systems and fertilization strategies (P > 0.05). Soybean shoot biomass and P uptake were, respectively, 25.3% and 29.7% higher in ICLS compared to CS (P < 0.05). For NDVI, an interaction between the production system and days after sowing (P < 0.05) was observed. NDVI increased to 0.95 at 96 days after sowing in CS and to 0.92 at 92 days after sowing in ICLS. A significant relationship between NDVI and shoot biomass, and P and K uptake was observed (P < 0.05). Our results show that the vegetation index NDVI can be used for estimating shoot biomass and P and K uptake in the early growth stages of soybean crops, providing farmers with a new tool for evaluating the spatial variability of soybean growth and nutrition.

Breeding for disease resistance in soybean: a global perspective

KEY MESSAGE

This review provides a comprehensive atlas of QTLs, genes, and alleles conferring resistance to 28 important diseases in all major soybean production regions in the world. Breeding disease-resistant soybean [Glycine max (L.) Merr.] varieties is a common goal for soybean breeding programs to ensure the sustainability and growth of soybean production worldwide. However, due to global climate change, soybean breeders are facing strong challenges to defeat diseases. Marker-assisted selection and genomic selection have been demonstrated to be successful methods in quickly integrating vertical resistance or horizontal resistance into improved soybean varieties, where vertical resistance refers to R genes and major effect QTLs, and horizontal resistance is a combination of major and minor effect genes or QTLs. This review summarized more than 800 resistant loci/alleles and their tightly linked markers for 28 soybean diseases worldwide, caused by nematodes, oomycetes, fungi, bacteria, and viruses. The major breakthroughs in the discovery of disease resistance gene atlas of soybean were also emphasized which include: (1) identification and characterization of vertical resistance genes reside rhg1 and Rhg4 for soybean cyst nematode, and exploration of the underlying regulation mechanisms through copy number variation and (2) map-based cloning and characterization of Rps11 conferring resistance to 80% isolates of Phytophthora sojae across the USA. In this review, we also highlight the validated QTLs in overlapping genomic regions from at least two studies and applied a consistent naming nomenclature for these QTLs. Our review provides a comprehensive summary of important resistant genes/QTLs and can be used as a toolbox for soybean improvement. Finally, the summarized genetic knowledge sheds light on future directions of accelerated soybean breeding and translational genomics studies.

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.

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.

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis–NIR Spectroscopy

Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory methods, leading to better disease management in the field. In this study, leaf spectra from healthy and diseased leaves were measured with a spectrometer. The diseased leaves were subjected to RT-qPCR for the detection and quantification of the titer of ToCV. Neighborhood component analysis (NCA) algorithm was employed for the feature selection of the effective wavelengths and the most important vegetation indices out of the 24 that were tested. Two machine learning methods, namely XY-fusion network (XY-F) and multilayer perceptron with automated relevance determination (MLP–ARD), were employed for the estimation of the disease existence and viral load in the tomato leaves. The results showed that before outlier elimination, the MLP–ARD classifier generally outperformed the XY-F network with an overall accuracy of 92.1% against 88.3% for the XY-F. Outlier elimination contributed to the performance of the classifiers as the overall accuracy for both XY-F and MLP–ARD reached 100%.

Detection of Stress Induced by Soybean Aphid (Hemiptera: Aphididae) Using Multispectral Imagery from Unmanned Aerial Vehicles

Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a common pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), in North America requiring frequent scouting as part of an integrated pest management plan. Current scouting methods are time consuming and provide incomplete coverage of soybean. Unmanned aerial vehicles (UAVs) are capable of collecting high-resolution imagery that offer more detailed coverage in agricultural fields than traditional scouting methods. Recently, it was documented that changes to the spectral reflectance of soybean canopies caused by aphid-induced stress could be detected from ground-based sensors; however, it remained unknown whether these changes could also be detected from UAV-based sensors. Small-plot trials were conducted in 2017 and 2018 where cages were used to manipulate aphid populations. Additional open-field trials were conducted in 2018 where insecticides were used to create a gradient of aphid pressure. Whole-plant soybean aphid densities were recorded along with UAV-based multispectral imagery. Simple linear regressions were used to determine whether UAV-based multispectral reflectance was associated with aphid populations. Our findings indicate that near-infrared reflectance decreased with increasing soybean aphid populations in caged trials when cumulative aphid days surpassed the economic injury level, and in open-field trials when soybean aphid populations were above the economic threshold. These findings provide the first documentation of soybean aphid-induced stress being detected from UAV-based multispectral imagery and advance the use of UAVs for remote scouting of soybean aphid and other field crop pests.

Leaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops

This study is aimed at (i) estimating the angular leaf spot (ALS) disease severity in common beans crops in Brazil, caused by the fungus Pseudocercospora griseola, employing leaf and canopy spectral reflectance data, (ii) evaluating the informative spectral regions in the detection, and (iii) comparing the estimation accuracy when the reflectance or the first derivative reflectance (FDR) is employed. Three data sets of useful spectral reflectance measurements in the 440 to 850 nm range were employed; measurements were taken over the leaves and canopy of bean crops with different levels of disease. A system based in Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) was developed to estimate the disease severity from leaf and canopy hyperspectral reflectance spectra. Levels of disease to be taken as true reference were determined from the proportion of the total leaf surface covered by necrotic lesions on RGB images. When estimating ALS disease severity in bean crops by using hyperspectral reflectance spectrometry, this study suggests that (i) successful estimations with coefficients of determination up to 0.87 can be achieved if the spectra is acquired by the spectroradiometer in contact with the leaves, (ii) unsuccessful estimations are obtained when the spectra are acquired by the spectroradiometer from one or more meters above the crop, (iii) the red to near-infrared spectral region (630–850 nm) offers the same precision in the estimation as the blue to near-infrared spectral region (440–850), and (iv) neither significant improvements nor significant detriments are achieved when the input data to the estimation processing system are the FDR spectra, instead of the reflectance spectra.

Effects of Foliar Insecticides on Leaf-Level Spectral Reflectance of Soybean

Pest-induced changes in plant reflectance are crucial for the development of pest management programs using remote sensing. However, it is unknown if plant reflectance data is also affected by foliar insecticides applied for pest management. Our study assessed the effects of foliar insecticides on leaf reflectance of soybean. A 2-yr field trial and a greenhouse trial were conducted using randomized complete block and completely randomized designs, respectively. Treatments consisted of an untreated check, a new systemic insecticide (sulfoxaflor), and two representatives of the most common insecticide classes used for soybean pest management in the north-central United States (i.e., λ-cyhalothrin and chlorpyrifos). Insecticides were applied at labeled rates recommended for controlling soybean aphid; the primary insect pest in the north-central United States. Leaf-level reflectance was measured using ground-based spectroradiometers. Sulfoxaflor affected leaf reflectance at some red and blue wavelengths but had no effect at near-infrared or green wavelengths. Chlorpyrifos affected leaf reflectance at some green, red, and near-infrared wavelengths but had no effect at blue wavelengths. λ-cyhalothrin had the least effect on spectral reflectance among the insecticides, with changes to only a few near-infrared wavelengths. Our results showing immediate and delayed effects of foliar insecticides on soybean reflectance indicate that application of some insecticides may confound the use of remote sensing for detection of not only insects but also plant diseases, nutritional and water deficiencies, and other crop stressors.

Tropical food legumes: virus diseases of economic importance and their control

Diverse array of food legume crops (Fabaceae: Papilionoideae) have been adopted worldwide for their protein-rich seed. Choice of legumes and their importance vary in different parts of the world. The economically important legumes are severely affected by a range of virus diseases causing significant economic losses due to reduction in grain production, poor quality seed, and costs incurred in phytosanitation and disease control. The majority of the viruses infecting legumes are vectored by insects, and several of them are also seed transmitted, thus assuming importance in the quarantine and in the epidemiology. This review is focused on the economically important viruses of soybean, groundnut, common bean, cowpea, pigeonpea, mungbean, urdbean, chickpea, pea, faba bean, and lentil and begomovirus diseases of three minor tropical food legumes (hyacinth bean, horse gram, and lima bean). Aspects included are geographic distribution, impact on crop growth and yields, virus characteristics, diagnosis of causal viruses, disease epidemiology, and options for control. Effectiveness of selection and planting with virus-free seed, phytosanitation, manipulation of crop cultural and agronomic practices, control of virus vectors and host plant resistance, and potential of transgenic resistance for legume virus disease control are discussed.