Abstract
Plant diseases are unfavourable factors that cause a significant decrease in the quality and quantity of crops. Experienced biologists or farmers often observe plants with the naked eye for disease, but this method is often imprecise and can take a long time. In this study, we use artificial intelligence and computer vision techniques to achieve the goal of designing and developing an intelligent classification mechanism for leaf diseases. This paper follows two methodologies and their simulation outcomes are compared for performance evaluation. In the first part, data augmentation is performed on the PlantVillage data set images (for apple, corn, potato, tomato, and rice plants), and their deep features are extracted using convolutional neural network (CNN). These features are classified by a Bayesian optimized support vector machine classifier and the results attained in terms of precision, sensitivity, f-score, and accuracy. The above-said methodologies will enable farmers all over the world to take early action to prevent their crops from becoming irreversibly damaged, thereby saving the world and themselves from a potential economic crisis. The second part of the methodology starts with the preprocessing of data set images, and their texture and color features are extracted by histogram of oriented gradient (HoG), GLCM, and color moments. Here, the three types of features, that is, color, texture, and deep features, are combined to form hybrid features. The binary particle swarm optimization is applied for the selection of these hybrid features followed by the classification with random forest classifier to get the simulation results. Binary particle swarm optimization plays a crucial role in hybrid feature selection; the purpose of this Algorithm is to obtain the suitable output with the least features. The comparative analysis of both techniques is presented with the use of the above-mentioned evaluation parameters.
Highlights
Diseases, pests, and other undesirable substances present in crops can cause a sharp decline in agricultural production [1]. e impact of these dangerous factors on crops has a direct impact on the decline of the quality and quantity of crops
AI has found a large number of applications in day-to-day life, leading to the introduction of the terms “machine learning” (ML) and “deep learning” (DL), which, in terms of simplicity, allows machines to “learn” a large number of patterns and take action
(2) AlexNet: aiming at the use of convolutional neural network architecture with good performance reported in the related works, this network is composed of five initial convolutional layers and three layers completely connected at the end to produce the classification
Summary
Pests, and other undesirable substances present in crops can cause a sharp decline in agricultural production [1]. e impact of these dangerous factors on crops has a direct impact on the decline of the quality and quantity of crops. “In another research, Lu and others [16] proposed the use of CNN for the identification of 10 common rice diseases, using natural images of healthy and diseased rice leaves and stems captured from the experimental field. Their model achieved an accuracy of 95.48%. 2. Proposed Methodology is research paper presents the detection of leaf diseases in corn, apple, tomato, rice, and potato leaves by extraction of deep features and texture and color features, followed by feature selection based on BPSO, comparing the two classifications: Bayesian optimized SVM and random forest.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
