Design and Analysis of a Machine learning based Agriculture bot

Abstract

The detrimental impact of diseases on plants is a longstanding challenge, causing considerable harm and economic losses in crop yields. Mitigating these losses necessitates early disease detection, significantly enhancing product quality. Furthermore, accurate diagnosis is crucial to avoid the improper use of pesticides. The proposed system aims to revolutionize disease diagnosis in plants through the integration of image processing and artificial intelligence techniques, specifically applied to images of plant leaves. The system unfolds in two pivotal phases. In the initial phase, the plant is identified based on distinctive leaf features. This process encompasses the preprocessing of leaf images and feature extraction. Subsequently, an Artificial Neural Network (ANN) is employed for training and classification, enabling the system to recognize and categorize different plant species. The second phase focuses on disease classification within the identified plant. This intricate process involves K-Means-based segmentation to isolate defected areas on the leaf. Feature removal is then executed on the affected portions to extract disease-specific attributes. The final step entails an ANN-based classification, where the system accurately identifies and categorizes the type and severity of the disease afflicting the plant. The synergy of image processing and artificial intelligence in these two phases empowers the system to provide a comprehensive diagnosis. By ensuring accurate plant recognition and precise disease classification, the proposed system not only aids in curbing economic losses but also contributes to sustainable agriculture practices by promoting judicious pesticide use. This innovative approach heralds a new era in plant disease management, fostering improved crop yields and sustainable agricultural practices.