Bacterial and virus diseases causes major damage in agriculture. Classifying those is an challenging but important task. This dataset contains +-274 000 spectral line images (512 pixels x 56 bands) of 6 different cultivars of healthy, bacterial (Erwinia) and virus (PVY) infected plants. The coordinates of each image is stored and can be compared with the stored position of individual plants labeled by crop experts

Data underlying the publication: Potato Virus Y Detection in Seed Potatoes Using Deep Learning on Hyperspectral Images

The rapid and precise detection of diseases and plant disorders is the basis for the adequate and timely design of management strategies. Currently, there are several non-destructive alternatives that allow early detection, highlighting the use of spectral cameras attached to unmanned aerial vehicles (UAVs). The objective of this research was to evaluate the use of multispectral cameras on UAVs to discriminate vascular wilt caused by Verticillium spp., (VW), waterlogging stress (WL), and an unknown alteration (UA) in commercial potato (Solanum tuberosum) variety “Diacol Capiro” crops. Plots were monitored during the crop cycle, performing the visual characterization of the diseases and disorders present. Five spectral band images were acquired using a MicaSense RedEdge spectral camera attached to a Map-T680 hexacopter drone to extract the bands and calculate the vegetation indices that were calibrated and evaluated to determine their ability to discriminate between diseased and healthy plants based on a generalized linear model (GLM) and Kappa index. Additionally, the supervised random forest classification method was implemented, optimized, and evaluated using the accuracy, area under receiver operating characteristic curve (ROC-AUC), kappa index, and inference error based on k-fold cross-validation. After algorithms optimization our results show a classifier accuracy, kappa and ROC-AUC values to VW, WL and UA between 73.5–82.5%, 0.56–0.71, 0.97–0.98, and 35 37.5–51.9%, 0.07–0.06, and 0.88–0.94 for plots 1 and 2, respectively. This study reports an approach to the use of multispectral cameras attached to UAVs as a tool with potential for the detection of diseases and physiological disorders in commercial potato crops.

Identification of diseases and physiological disorders in potato via multispectral drone imagery using machine learning tools

Sugarcane white leaf phytoplasma (white leaf disease) in sugarcane crops is caused by a phytoplasma transmitted by leafhopper vectors. White leaf disease (WLD) occurs predominantly in some Asian countries and is a devastating global threat to sugarcane industries, especially Sri Lanka. Therefore, a feasible and an effective approach to precisely monitoring WLD infection is important, especially at the early pre-visual stage. This work presents the first approach on the preliminary detection of sugarcane WLD by using high-resolution multispectral sensors mounted on small unmanned aerial vehicles (UAVs) and supervised machine learning classifiers. The detection pipeline discussed in this paper was validated in a sugarcane field located in Gal-Oya Plantation, Hingurana, Sri Lanka. The pixelwise segmented samples were classified as ground, shadow, healthy plant, early symptom, and severe symptom. Four ML algorithms, namely XGBoost (XGB), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN), were implemented along with different python libraries, vegetation indices (VIs), and five spectral bands to detect the WLD in the sugarcane field. The accuracy rate of 94% was attained in the XGB, RF, and KNN to detect WLD in the field. The top three vegetation indices (VIs) for separating healthy and infected sugarcane crops are modified soil-adjusted vegetation index (MSAVI), normalized difference vegetation index (NDVI), and excess green (ExG) in XGB, RF, and DT, while the best spectral band is red in XGB and RF and green in DT. The results revealed that this technology provides a dependable, more direct, cost-effective, and quick method for detecting WLD.

/pdf/detection-of-white-leaf-disease-in-sugarcane-using-machine-bjgbu08k.pdf

Detection of White Leaf Disease in Sugarcane Using Machine Learning Techniques over UAV Multispectral Images

Tools for robust identification of crop diseases are crucial for timely intervention by farmers to minimize yield losses. Visual diagnosis of crop diseases is time-consuming and laborious, and has become increasingly unsuitable for the needs of modern agricultural production. Recently, deep convolutional neural networks (CNNs) have been used for crop disease diagnosis due to their rapidly improving accuracy in labeling images. However, previous CNN studies have mostly used images of single leaves photographed under controlled conditions, which limits operational field use. In addition, the wide variety of available CNNs and training options raises important questions regarding optimal methods of implementation of CNNs for disease diagnosis. Here, we present an assessment of seven typical CNNs (VGG-16, Inception-v3, ResNet-50, DenseNet-121, EfficentNet-B6, ShuffleNet-v2 and MobileNetV3) based on different training strategies for the identification of wheat main leaf diseases (powdery mildew, leaf rust and stripe rust) using field images. We developed a Field-based Wheat Diseases Images (FWDI) dataset of field-acquired images to supplement the public PlantVillage dataset of individual leaves imaged under controlled conditions. We found that a transfer-learning method employing retuning of all parameters produced the highest accuracy for all CNNs. Based on this training strategy, Inception-v3 achieved the highest identification accuracy of 92.5% on the test dataset. While lightweight CNN models (e.g., ShuffleNet-v2 and MobileNetV3) had shorter processing times (<0.007 s per image) and smaller memory requirements for the model parameters (<20 MB), their accuracy was relatively low (~87%). In addition to the role of CNN architecture in controlling overall accuracy, environmental effects (e.g., residual water stains on healthy leaves) were found to cause misclassifications in the field images. Moreover, the small size of some target symptoms and the similarity of symptoms between some different diseases further reduced the accuracy. Overall, the study provides insight into the collective effects of model architecture, training strategies and input datasets on the performance of CNNs, providing guidance for robust CNN design for timely and accurate crop disease diagnosis in a real-world environment.

/pdf/evaluation-of-diverse-convolutional-neural-networks-and-1dnhf7jk.pdf

Evaluation of Diverse Convolutional Neural Networks and Training Strategies for Wheat Leaf Disease Identification with Field-Acquired Photographs

• This paper contributes to the scientific community engaged in plant disease sensing: • An end-to-end ensemble modeling approach for classifying Verticillium wilt severity in potato crop fields • An application of an optimized machine learning technique which provides accurate estimations on unseen unbalanced data • An image analysis workflow based on one-square meter cells rather than on individual pixels Potato Verticillium wilt is a harmful and widespread disease which causes losses between 20% and 50% of crop production in Colombia. Diagnostics of Verticillium wilt in potato crops is challenging due that the known symptoms are often associated with other diseases. While recent studies have explored the potential of high spatial resolution imagery for detection of diseases and physiological disorders in potato crops, results are not-conclusive yet. Thus, to contribute to improving identification of Verticillium wilt symptoms, we evaluate a machine learning method to classify disease severity from multidate UAV-based multispectral images. It uses an ensemble of gradient boosting machines (GBMs) to differentiate, at grid level, six levels of Verticillim wilt disease severity. The proposed method was tested in a commercial potato crop in Cundinamarca, one of the top potato production regions in Colombia, using multispectral images acquired in four different field campaigns conducted between 99 and 141 days after planting. Accuracy assessment of results indicates that classification of severity levels of Verticillium wilt infection was satisfactory with F1 scores of 82% for the training dataset and 84% for the testing dataset. This study results shows both potential and challenges of using machine learning ensemble methods for identification of Verticillium wilt symptoms in potato crops from high spatial resolution multispectral images. 

Identification of symptoms related to potato Verticillium wilt from UAV-based multispectral imagery using an ensemble of gradient boosting machines

Machine learning (ML) and its multiple applications have comparative advantages for improving the interpretation of knowledge on different agricultural processes. However, there are challenges that impede proper usage, as can be seen in phenotypic characterizations of germplasm banks. The objective of this research was to test and optimize different analysis methods based on ML for the prioritization and selection of morphological descriptors of Rubus spp. 55 descriptors were evaluated in 26 genotypes and the weight of each one and its ability to discriminating capacity was determined. ML methods as random forest (RF), support vector machines, in the linear and radial forms, and neural networks were optimized and compared. Subsequently, the results were validated with two discriminating methods and their variants: hierarchical agglomerative clustering and K-means. The results indicated that RF presented the highest accuracy (0.768) of the methods evaluated, selecting 11 descriptors based on the purity (Gini index), importance, number of connected trees, and significance (p value < 0.05). Additionally, K-means method with optimized descriptors based on RF had greater discriminating power on Rubus spp., accessions according to evaluated statistics. This study presents one application of ML for the optimization of specific morphological variables for plant germplasm bank characterization.

https://www.mdpi.com/2223-7747/10/2/247/pdf

Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks

Abstract The spittlebug ( Aeneolamia varia ) is one of the most important sugarcane pests in Colombia, where a recent increase in population and distribution specially in southwestern Colombia have led to the need for new technologies for integrated pest management. The objectives of this study were to determine the spatial distribution of this pest in commercial sugarcane fields and to validate machine learning (ML) tools for indirect injury detection and impact on yield (damage) using satellite images. This study was carried out in fields grown with the CC 01-1940 variety in El Cerrito, Valle del Cauca, Colombia, where systematic sampling of the populations (number of adults and nymphs per stem) was carried out. The spatial aggregation and distribution were determined using Moran’s index and point patterns, sequence observations, and analysis with distance indicators (Sadie). The indirect injury detection and quantification of the impact on production were carried out with a ML approach using satellite image products with 10 m spatial and five days temporal resolutions, obtained from a Sentinel-2 sensor using Google Earth Engine. The results indicated that spittlebug populations had an aggregate spatial behavior and high spatial dependence. In addition, the ML algorithms predicted spittlebug injury, and the effect on production was estimated at 26.4 tons of cane per hectare, which represented a 17% reduction in the expected yield. The use of spatial analysis and remote sensing tools are an alternative for indirect detection of injury and for understanding population dynamics of the pest in sugarcane, so they can become instrumental for decision-making on an integrated pest management program. 

/pdf/population-dynamics-and-estimation-of-damage-of-the-2s80e2on.pdf

Population Dynamics and Estimation of Damage of the Spittlebug Aeneolamia varia on Sugarcane in Colombia by Using remote Sensing and Machine Learning Tools

William Alfonso Leon-Rueda

Papers

Identification of diseases and physiological disorders in potato via multispectral drone imagery using machine learning tools

Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks

Population Dynamics and Estimation of Damage of the Spittlebug Aeneolamia varia on Sugarcane in Colombia by Using remote Sensing and Machine Learning Tools