Performance Analysis of First Order Optimizers for Plant Pest Detection Using Deep Learning

Abstract

AbstractSeveral of the major issues affecting food productivity are a pest. The timely and precise detection of plant pests is crucial for avoiding the loss of agricultural productivity. Only by detecting the pest at an early stage can it be controlled. Due to the cyclical nature of agriculture, pest accumulation and variety might vary from season to season, rendering standard approaches for pest classification and detection ineffective. Methods based on machine learning can be utilized to resolve such issues. Deep Learning, which has become extremely popular in image processing, has recently opened up a plethora of new applications for smart agriculture. Optimizers are primarily responsible for the process of strengthening the deep learning model’s pest detection capabilities. In order to detect pests on tomato plants, this study compares the performance of a few gradient-based optimizers, including stochastic gradient descent, root means square propagation, adaptive gradient, and adaptive moment estimation, on a proposed deep convolution neural network architecture with augmented data. In comparison to other optimizers, the evaluation findings demonstrate that the Adam optimizer performs better with an accuracy of 93% for pest identification.KeywordsDeep LearningOptimizersConvolution Neural Network