Abstract:
This report focuses on advancements in cotton leaf disease detection, aiming to address
agricultural challenges and promote sustainable farming practices. Leveraging deep
learning models such as VGG16 (93.80% accuracy), ResNet152 (90.80% accuracy),
and InceptionV3 (96.40% accuracy), this research introduces a web-based tool for
accurate disease identification in cotton plants. Manual methods often lead to
inconsistencies, highlighting the need for automated solutions. The integration of
convolutional neural networks (CNNs) into a user-friendly application enables precise
disease detection, contributing to improved crop management and yield optimization.
The project's objective is to overcome limitations in traditional methods by harnessing
the power of deep learning, offering benefits such as reduced labor, minimized errors,
and increased productivity. Through the development of an accessible application,
farmers can make informed decisions, leading to enhanced crop health and sustainable
agricultural practices. Additionally, the project aims to provide educational resources
and establish a feedback mechanism for continuous improvement, fostering
collaboration and knowledge sharing within the agricultural community. This research
pioneer’s transformative technology in agriculture, specifically targeting cotton leaf
disease detection, with far-reaching implications for sustainable farming practices.
