OPTIMIZING PADDY CULTIVATION: A FUZZY LOGIC APPROACH TO PADDY GROWTH ANALYSIS AND DISEASE DETECTION

Abstract

Agriculture is the primary livelihood for around 60% of India's population, yet it contributes only 16% to the country's GDP. Despite being the largest employment sector, Indian agriculture requires innovative solutions to improve both the welfare of farmers and the productivity of agricultural land. The sector faces a pressing need for sustainable, efficient, and technology-driven farming practices. This project focuses on improving paddy cultivation, which is divided into three different varieties. By utilizing key parameters of agricultural land, such as temperature, humidity, sunlight, and soil moisture, we apply fuzzy logic membership functions to generate outputs like water irrigation, plant growth monitoring, and disease detection. Fuzzy logic is employed to calculate these outputs based on the input parameters. Additionally, IR cameras are used to monitor and analyse plant growth effectively. This approach addresses the fundamental needs of agricultural land and promotes more productive farming practices. The central objective of this project is to enhance the cultivation of three distinct paddy varieties through advanced monitoring and decision-making tools.