AI-Driven Crop Management System with Integrated Farmer Support: Price Insights, Article Recommendations, and Disease Detection via Image Recognition

Agriculture remains the backbone of many economies, especially in developing regions, where a significant portion of the population depends on farming for their livelihood. However, farmers often face multiple challenges & hurdles, including volatile crop prices, limited access to trustworthy agricultural information, and delayed or wrong diagnosis of crop diseases. These challenges hinder productivity, profitability, and sustainability. In response to these crucial needs, this research introduces an advanced AI-driven Crop Management System designed to empower farmers through a comprehensive digital platform. The system integrates real-time crop price tracking, informational article recommendations, and a powerful crop disease diagnostic tool using state-of-the-art image recognition tools and techniques. The core objective of this system is to leverage artificial intelligence and machine learning technologies to enhance agricultural decision-making, reduce crop losses, and improve farmers’ access to vital information. One of the primary modules of the system is the Crop Price Monitoring Feature, which fetches and displays real-time price information for a variety of crops across multiple regions. This data is sourced from agricultural markets and government databases to ensure accuracy, reliability and timeliness of the data provided. By presenting this information through a farmer-friendly interface, the system helps farmers make informed decisions about when and where to sell their produce for optimal returns in the local market. Another integral component is the Article Recommendation Engine, which uses A.I to deliver tailored content based on user activity, location, crop preferences, and farming practices. This includes best practices related to specific crops, climate conditions, and seasonal patterns. The recommendation system employs collaborative and content-based filtering methods to ensure that the information presented is both relevant and practical. By continuously learning from user interactions, the engine improves over time, offering increasingly personalized suggestions that can directly support better farm management. Perhaps the most innovative feature of the platform is the Crop Doctor Module, which enables farmers to diagnose plant diseases through image recognition. Farmers can capture and upload images of affected plants using a smartphone, and the system processes these images using convolutional neural networks (CNNs), a subset of deep learning optimized for visual data analysis. The model is trained on a large dataset of annotated plant disease images covering a variety of crops such as rice, wheat, tomato, and maize. Once an image is processed, the system identifies the disease, estimates its severity, and suggests possible treatments or preventive actions. In cases where image recognition is inconclusive, the system can redirect the query to an expert or community forum for further review. To ensure accessibility, the system is designed with a mobile-first interface and supports multiple local languages. This allows rural farmers, who may not have access to computers or fluency in English, to benefit from the full range of services offered. Offline capabilities are also considered to ensure basic features like saved articles and past diagnoses remain accessible without internet connectivity. The user interface prioritizes simplicity, with intuitive icons and navigation that accommodate users with limited digital literacy.