Analyzing Deep Learning Architectures in Cotton Crop for Precision Disease Diagnosis
subject: Convolutional Neural Networks (CNN); Cotton Disease; Precision Agriculture; ResNet18; VGG16
description: Cotton is an important cash crops worldwide, providing raw materials for the textile industry and is the basis of livelihood of millions of farmers. In India, it has an important place in the agricultural economy, which contributes significantly to both domestic consumption and export income. However, cotton production is highly sensitive to infection of various diseases and insects, such as bacterial scorching, powdery mildew and targeted spots, which can cause severe yield reduction and economic loss. Traditional disease management methods often depend on manual inspection, which is difficult to scale in time consuming, human error and large cultivated areas. Therefore, it is necessary to detect the initial and accurate detection of the disease to ensure plant health and maximize productivity. This study examines advanced intensive teaching methods for automatic cotton disease diagnosis, and compare the performance of VGG16 and ResNet18 architecture. Experimental results showed that the VGG16 model achieved verification accuracy of 99.69%, while ResNet18 achieved an accuracy of 99.58%. In addition, a real time forecasting interface was developed from the URL provided by the user to classify images of cotton leaves, making practical signs possible for use in the area. This research highlights effectiveness of deep learning in improving accurate agriculture, which helps in timely detection of diseases to reduce the loss of crops. 2025 IEEE.
creator: Bomble, Shivam Vijay; Nizar Banu, P.K.
source: 2025 17th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2025;pp.337-344
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833158733-8;
language: English
type: Conference paper
coverage: Bomble S.V., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Nizar Banu P.K., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India