AgriTech AI: Plant Disease Device

Abstract

Agricultural productivity is significantly affected by plant diseases, which can reduce crop yield, quality, and economic returns. Timely identification of disease symptoms is essential for effective crop management and sustainable farming practices. Traditional disease detection methods primarily rely on manual observation and expert consultation, which may be time-consuming, costly, and inaccessible in many farming regions. To address these challenges, this study proposes an intelligent plant disease detection and crop health monitoring system based on Artificial Intelligence and computer vision techniques.

The proposed system utilizes digital image acquisition, image preprocessing, feature extraction, and deep learning-based classification to identify plant diseases from leaf images. A camera module captures images of plant leaves, and the acquired images are processed through a series of enhancement and normalization techniques. A Convolutional Neural Network (CNN) model is employed to learn disease-related patterns and classify plant conditions accurately. The system is designed to recognize multiple disease categories while also distinguishing healthy leaves from infected ones.

In addition to disease identification, the proposed framework provides treatment recommendations and preventive guidelines to assist farmers in taking timely corrective actions. The portable design of the system enables deployment in agricultural fields, greenhouses, and research environments. The integration of intelligent decision-support mechanisms contributes to reducing crop losses, optimizing resource utilization, and improving agricultural productivity.

The proposed approach demonstrates the potential of Artificial Intelligence in transforming conventional farming practices into data-driven and technology-enabled agricultural systems. By facilitating rapid and reliable disease diagnosis, the system supports precision agriculture and promotes sustainable crop management strategies.