Supervised Deep Learning based Leaf Disease and Pest Detection using Image Processing
Deva Priya Isravel, Kumaresan Somasundaram, M Jestin Josephraj, L Christopher Paul, J.E. Johnson
Abstract
The key problems that influence plant health and crop yield quality are leaf disease and pests. To improve crop production many advanced technologies are deployed. One of the predominant technologies is the incorporation of Deep Learning (DL) techniques. DL supports numerous training and testing models that are suitable for smart agriculture. Real-time detection of plant diseases and pest detection is made simpler and more efficient by using DL models. This paper provides an extensive survey of the DL techniques associated with agriculture automation and discusses the latest models focusing on leaf disease detection and pest identification. To select the best features and to optimize the accuracy of the results, an optimizer is identified and an enhanced deep learning model is proposed. The VGG16 and YOLOV5s models are deployed with ADAM optimizer. The results illustrate that the proposed optimized approach achieves an accuracy of 98.71% for leaf disease detection and 97.52% for pest detection.