AI-Powered Crop Care: Transforming Farming with Disease Detection and Sustainable Practices

Abstract

introduction: This paper offers an AI-powered platform designed to transform agricultural practices thru superior crop disease detection. the usage of deep learning models, the platform identifies plant illnesses with high accuracy, enabling well timed interventions to limit yield losses. It features a user-pleasant interface and multilingual guide to make sure accessibility for diverse farming groups. by delivering actual-time crop health insights, the platform empowers farmers to make knowledgeable decisions, enhancing productivity, sustainability, and profitability. This study highlights the platform's ability to revolutionize sickness control and sell resilient farming practices.

Targets: This study develops a deep getting to know-primarily based machine for forecasting, detecting, and classifying plant leaf sicknesses. It predicts outbreaks by way of figuring out early signs and utilizes ResNet-50, VGG16, and VGG19 for correct category. switch studying, hyperparameter tuning, and rigorous assessment beautify performance, contributing to progressed crop protection and food security.

Strategies: This research integrates advanced device gaining knowledge of and deep getting to know techniques to decorate agricultural selection-making via crop disease detection, crop pointers, and market charge forecasting. The proposed platform follows a established pipeline: information series, version development, machine integration, and deployment. Publicly to be had datasets, which include PlantVillage, undergo preprocessing to improve version generalization. For disease detection, a ResNet-50, VGG16 & VGG19 primarily based CNN, satisfactory-tuned via transfer learning and hyperparameter optimization, ensures high classification accuracy.

Outcomes: The 3-degree detection version performed excessive accuracy in identifying plant illnesses, with the exceptional-appearing approach achieving 99% checking out accuracy and 94% validation accuracy. The gadget tested robust generalization, reducing overfitting whilst improving precision and bear in mind.

Conclusions: The AI crop disorder detection platform offers a transformative solution for sustainable farming by using enabling early disease detection, decreasing pesticide use, and enhancing productivity. Its actual-time insights aid knowledgeable selection-making, and destiny integration with precision agriculture technology ought to further enlarge its effect. This study highlights AI's potential in revolutionizing present-day farming with scalable and reachable solutions.