Mobile Application for Chili Disease Detection, Correction, Prevention and Price Prediction

This research paper introduces a mobile application that aims to revolutionize chili cultivation practices by employing machine learning for disease detection, correction, prevention, growth stage, harvest date and price prediction. In the world of agriculture, timely and informed decisions are crucial for the health and productivity of chili crops which require efficient cultivation practices. The research comprises gathering data on chili diseases, agrochemicals, fertilizers and prices from various sources including research papers, databases, and agricultural experts and preprocessing that data to ensure its quality and consistency, involving cleaning, transformation, feature engineering, outlier detection, and normalization. Machine learning models, particularly Random Forest, Polynomial Regression and Artificial Neural Network are developed using this data to identify the key factors affecting chili growth and yield. The solution aims to provide user-friendly recommendations for sustainable and efficient chili cultivation practices. It advises on the type and quantity of agrochemicals and fertilizers upon identifying chili plant diseases, enabling farmers to take expedient actions. Application testing including unit, integration and system testing are performed ensuring reliability, accuracy, and practicality. This research aims to empower chili farmers with insights and actionable recommendations, thus contributing significantly to improving crop health, reducing disease incidence and optimizing growth and harvests.