Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review

<span lang="EN-US">A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.</span>

Discovery of potential protein biomarkers associated with sugarcane white leaf disease susceptibility using a comparative proteomic approach

Sugarcane white leaf disease (SCWLD) is caused by phytoplasma, a serious sugarcane phytoplasma pathogen, which causes significant decreases in crop yield and sugar quality. The identification of proteins involved in the defense mechanism against SCWLD phytoplasma may help towards the development of varieties resistant to SCWLD. We investigated the proteomes of four sugarcane varieties with different levels of susceptibility to SCWLD phytoplasma infection, namely K88-92 and K95-84 (high), KK3 (moderate), and UT1 (low) by quantitative label-free nano-liquid chromatography-tandem mass spectrometry (nano LC-MS/MS). A total of 248 proteins were identified and compared among the four sugarcane varieties. Two potential candidate protein biomarkers for reduced susceptibility to SCWLD phytoplasma were identified as proteins detected only in UT1. The functions of these proteins are associated with protein folding, metal ion binding, and oxidoreductase. The candidate biomarkers could be useful for further study of the sugarcane defense mechanism against SCWLD phytoplasma, and in molecular and conventional breeding strategies for variety improvement.

Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection with Hardware Deployment

Smart agriculture has taken more attention during the last decade due to the bio-hazards of climate change impacts, extreme weather events, population explosion, food security demands and natural resources shortage. The Egyptian government has taken initiative in dealing with plants diseases especially tomato which is one of the most important vegetable crops worldwide that are affected by many diseases causing high yield loss. Deep learning techniques have become the main focus in the direction of identifying tomato leaf diseases. This study evaluated different deep learning models pre-trained on ImageNet dataset such as ResNet50, InceptionV3, AlexNet, MobileNetV1, MobileNetV2 and MobileNetV3.To the best of our knowledge MobileNetV3 has not been tested on tomato leaf diseases. Each of the former deep learning models has been evaluated and optimized with different techniques. The evaluation shows that MobileNetV3 Small has achieved an accuracy of 98.99% while MobileNetV3 Large has achieved an accuracy of 99.81%. All models have been deployed on a workstation to evaluate their performance by calculating the prediction time on tomato leaf images. The models were also deployed on a Raspberry Pi 4 in order to build an Internet of Things (IoT) device capable of tomato leaf disease detection. MobileNetV3 Small had a latency of 66 ms and 251 ms on the workstation and the Raspberry Pi 4, respectively. On the other hand, MobileNetV3 Large had a latency of 50 ms on the workstation and 348 ms on the Raspberry Pi 4.