Cotton Guard: Revolutionizing Agriculture with Smart Disease Management for Enhanced Productivity and Sustainability

Abstract

Cotton stands as a pivotal crop globally, offering fibers for clothing, feed for livestock, and oil for diverse industries Nonetheless, its production faces a myriad of threats from various diseases that jeopardize yield and quality. To address this challenge, a proposed model advocates for the creation of an application merging Flutter, a cross-platform mobile development framework, and Convolutional Neural Network (CNN) for efficient disease identification in cotton plants. The proposed system aims to design a user-friendly interface, train a CNN model with a substantial dataset to achieve an average of 85% accuracy, integrate the model with Flutter for both online and offline disease identification, and assess the app's usability across diverse user groups. The envisioned systems hold transformative potential in Precision Agriculture, enabling swift disease identification for targeted interventions and reduced reliance on broad-spectrum pesticides. It serves as a Crop Monitoring Service for experts, aids in farmer education on cotton diseases, and contributes to research and development by collecting data on disease patterns. Furthermore, it enhances Crop Insurance Assessment, expediting claims processing with accurate disease identification. Positioned as a pioneering solution for cotton disease management, the app promises to elevate crop productivity, quality, and profitability, while concurrently enhancing the knowledge and skills of stakeholders in the agricultural sector.