Optimization of Deep Learning Model for Plant Disease Detection Using Particle Swarm Optimizer

Abstract

Plant diseases are a major impendence to food security, and due to a lack of key infrastructure in many regions of the world, quick identification is still challenging. Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities, motivating our mission. Because of the large range of diseases, identifying and classifying diseases with human eyes is not only time-consuming and labor intensive, but also prone to being mistaken with a high error rate. Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and diagnosis. The proposed work describes a deep learning approach for detection plant disease. Therefore, we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf diseases. In our research, we focused on detecting plant diseases in five crops divided into 25 different types of classes (wheat, cotton, grape, corn, and cucumbers). In this task, we used a public image database of healthy and diseased plant leaves acquired under realistic conditions. For our work, a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics (accuracy, specificity, Sensitivity, precision, and Fscore) of the tested deep learning networks achieves an accuracy of 98.83%, specificity of 98.56%, Sensitivity of 98.78%, precision of 98.67%, and F-score of 98.47%, demonstrating the feasibility of this approach.

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An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

“…CNNs and recurrent neural networks (RNN) are the most common deep learning models [ 40 ]. Their variations make up the majority of existing deep-learning models.…”

The Novel ML Approaches on Wheat Disease by CLF and FOHF of Image Equalization Techniques

“…Nonetheless, it faces numerous obstacles, such as a complicated background , a high degree of similarity, and the segmentation of regions. [1][2][3]There are two types of methods for identifying crop diseases and pests. Elaraby et al [1] suggested a deep-learning technique for recognising 25 distinct plant diseases.…”

“…Manual experience is required, and the procedure is time-consuming and labor-intensive. Due to the wide variety of pests and unequal distribution of disease spots, it is challenging for specialists to undertake timely and accurate screening [1][2][3]. Consequently, precise disease severity estimation aids in wheat disease control and prevention.…”