Crop Recommendation with Fertilizer Suggestion and Plant Disease Detection

Abstract

In modern agriculture, the convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has emerged as a promising solution to address challenges related to crop management, fertilizer optimization, and plant disease detection. This paper presents an integrated system that leverages IoT sensors, AI algorithms, and machine learning models to provide comprehensive support for farmers in making informed decisions regarding crop cultivation. The proposed system comprises three main components: crop recommendation, fertilizer suggestion, and plant disease detection. Firstly, utilizing IoT sensors deployed in the fields, environmental data such as temperature, humidity, soil moisture, and nutrient levels are continuously monitored and collected. This data is then fed into AI algorithms to analyze the optimal conditions for various crops. Based on historical data, weather forecasts, and crop characteristics, the system generates personalized crop recommendations for farmers, taking into account factors such as soil type, climate, and market demand.

Secondly, the system provides intelligent fertilizer suggestions tailored to the specific needs of each crop and soil condition. By analyzing soil nutrient levels, crop growth stages, and environmental factors, AI algorithms determine the appropriate type and amount of fertilizer required to maximize yield while minimizing environmental impact and input costs. This dynamic approach to fertilizer management aims to enhance nutrient utilization efficiency and promote sustainable agricultural practices. Thirdly, the system incorporates AI-based image processing techniques for early detection and diagnosis of plant diseases. High-resolution images of crop leaves captured by IoT-enabled cameras are analyzed using deep learning models trained on extensive datasets of diseased and healthy plants. By identifying visual symptoms and patterns associated with various diseases, the system provides timely alerts to farmers, enabling proactive disease management strategies such as targeted spraying and crop rotation. Overall, the integration of IoT and AI technologies offers a powerful framework for optimizing crop production, improving resource efficiency, and mitigating the risks associated with pest and disease outbreaks. Through real-time monitoring, data-driven insights, and proactive interventions, the proposed system empowers farmers to achieve higher yields, reduce input costs, and contribute to sustainable agriculture practices.