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


Saranya, T. and Deisy, C. and Sridevi, S. and Sonai Muthu Anbananthen, Kalaiarasi and Khan, M. K. A. Ahamed (2023) Performance Analysis of First Order Optimizers for Plant Pest Detection Using Deep Learning. Communications in Computer and Information Science, 1763. pp. 37-52. ISSN 1865-0929

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Several 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.

Item Type: Article
Uncontrolled Keywords: Deep Learning Optimizers Convolution Neural Network
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 28 Mar 2023 06:46
Last Modified: 27 Apr 2023 13:14


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