Interpretable Deep Learning applied to Plant Stress Phenotyping

Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and minute traits in applicable fields of biological sciences, is scarce. Here we consider one such real world example viz., accurate identification, classification and quantification of biotic and abiotic stresses in crop research and production. Up until now, this has been predominantly done manually by visual inspection and require specialized training. However, such techniques are hindered by subjectivity resulting from inter- and intra-rater cognitive variability. Here, we demonstrate the ability of a machine learning framework to identify and classify a diverse set of foliar stresses in the soybean plant with remarkable accuracy. We also present an explanation mechanism using gradient-weighted class activation mapping that isolates the visual symptoms used by the model to make predictions. This unsupervised identification of unique visual symptoms for each stress provides a quantitative measure of stress severity, allowing for identification, classification and quantification in one framework. The learnt model appears to be agnostic to species and make good predictions for other (non-soybean) species, demonstrating an ability of transfer learning

Optimization techniques on fuzzy inference systems to detect Xanthomonas campestris disease

This paper shows the outcomes for four optimization models based on fuzzy inference systems, intervened using Quasi-Newton and genetic algorithms, to early assess bean plants’ leaves for Xanthomonas campestris disease. The assessment on the status of the plant (sane or ill) is defined through the intensity of the color in the RGB scale for the data-sets and images to analyze the implementation of the models. The best model performance is 99.68% when compared with the training data and a 94% effectiveness rate on the detection of Xanthomonas campestris in a bean leave image. Therefore, these results would allow farmers to take early measures to reduce the impact of the disease on the look and performance of green bean crops

Managing The Tomato Leaf Disease Detection Accuracy Using Computer Vision Based Deep Neural Network

Development of leaf disease in the agricultural sector would decrease crop yield output. Thus, leaf disease identification can be achieved in an automatic way to increase the yield in the agriculture sector. However, most of the disease recognition system works with poor disease recognition due to varying patterns of leaf disease which impair detection accuracy. In this article, we are managing this issue by designing a computer vision model that assists in building a system that involves real-time image detection, feature extraction and image classification. The findings are given by the classifier, whether the leaf is diseased or not. In this paper we use Deep Neural Network (DNN) for real-time image classification. The experimental findings on tomato plant indicate that classification rates have increased with the proposed system relative to other current methods

Сверточные нейронные сети в задачах мониторинга состояния сельскохозяйственной растительности по данным аэрофотосъемки

В данной работе рассматривается задача распознавания состояния сельскохозяйственной раститель-ности по данным аэрофотосъемки различного пространственного разрешения. В качестве основы для рас-познавания используется классификатор, позволяющий осуществлять классификацию входного изображе-ния на три класса: «здоровая растительность», «пораженная растительность» и «почва». Предложенный классификатор строится из двух сверточных нейронных сетей, позволяющих выполнять классификацию на два класса: «здоровая растительность» и «пораженная растительность», «растительность» и «почва».In the article a recognition task of agricultural vegetation using aerial images of different spatial resolution is considered. An image classifier is proposed that allows classifying image segments into three classes: “healthy vegeta-tion”, “diseased vegetation” and “soil”. This classifier is implemented by two convolution neural networks that previ-ously form two classes of vegetation state: “healthy vegetation”-“diseased vegetation” and “vegetation”-“soil”

Image set for deep learning: field images of maize annotated with disease symptoms

Objectives Automated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers’ fields. However, it is difficult for a simple algorithm to distinguish between the target disease and other sources of dead plant tissue in a typical field, especially given the many variations in lighting and orientation. Training a machine learning algorithm to accurately detect a given disease from images taken in the field requires a massive amount of human-generated training data. Data description This data set contains images of maize (Zea mays L.) leaves taken in three ways: by a hand-held camera, with a camera mounted on a boom, and with a camera mounted on a small unmanned aircraft system (sUAS, commonly known as a drone). Lesions of northern leaf blight (NLB), a common foliar disease of maize, were annotated in each image by one of two human experts. The three data sets together contain 18,222 images annotated with 105,705 NLB lesions, making this the largest publicly available image set annotated for a single plant disease

Detection of Disease on Corn Plants Using Convolutional Neural Network Methods

Deep Learning is still an interesting issue and is still widely studied. In this study Deep Learning was used for the diagnosis of corn plant disease using the Convolutional Neural Network (CNN) method, with a total dataset of 3.854 images of diseases in corn plants, which consisted of three types of corn diseases namely Common Rust, Gray Leaf Spot, and Northern Leaf Blight. With an accuracy of 99%, in detecting disease in corn plants

Convolutional neural network for maize leaf disease image classification

This article discusses the maize leaf disease image classification. The experimental images consist of 200 images with 4 classes: healthy, cercospora, common rust and northern leaf blight. There are 2 steps: feature extraction and classification. Feature extraction obtains features automatically using convolutional neural network (CNN). Seven CNN models were tested i.e AlexNet, virtual geometry group (VGG) 16, VGG19, GoogleNet, Inception-V3, residual network 50 (ResNet50) and ResNet101. While the classification using machine learning methods include k-Nearest neighbor, decision tree and support vector machine. Based on the testing results, the best classification was AlexNet and support vector machine with accuracy, sensitivity, specificity of 93.5%, 95.08%, and 93%, respectively

Decision support system for plant and crop treatment and protection based on wireless sensor networks

A decision support system (DSS) able to protect/treat plants and crops taking into account the temporal and spatial variability of physical, environmental, and agricultural parameters has been described. It is based on remote sensing and the most sophisticated machine learning techniques: Gaussian processes and deep neural networks. An example of knowledge extraction and actionable rule definition has been presented too

The use of an extended set of key texture features Haralick in the diagnosis of plant diseases on leaf images

Proposed method based on fuzzy logic, including the operation of calculating the 36 key parameters of the R, G, B, RG, RB, GB color components of the original RGB color space images of plant leaves and the GLCM adjacency matrix, forming fuzzy conclusions about the type of plant disease, defusing using threshold binarization and majority voting on 6 parameters