Crop Suggestion and Disease Detection System using Random Forest and Convolutional Neural Network

Abstract

This study provides an extensive web and mobile-based application aimed at improving agriculture practice using crop suggestion, disease prediction, and farmer query answering. The system applies machine learning, image processing, and natural language processing concepts to present recommendatory information to farmers. Random Forest is applied for crop prediction with an accuracy rate of 99.14%, while CNN is applied for disease prediction. Moreover, an expert-consulting system through real-time chat-based query resolution links farmers with experts. This technology-based solution is designed to enhance crop yield, optimize farm decisions, and increase accessibility for farmers.

Introduction

Agriculture is vital for global food security and employment, but farmers often face challenges in crop selection, disease identification, and accessing expert advice. The UPAJ system addresses these issues by integrating machine learning and AI technologies into a single platform to improve farming efficiency and sustainability.

The system includes three main modules:

1. Crop Suggestion – Uses a Random Forest algorithm to recommend crops based on soil data, weather, and past yields, achieving 99.14% accuracy.

2. Disease Detection – Employs Convolutional Neural Networks (CNN) to identify plant diseases from images with 95.2% accuracy, enabling early intervention.

3. Farmer Query Resolution – Provides real-time expert advice through an AI chatbot and live communication, responding in under five seconds.

Built with technologies like Node.js, MongoDB, Firebase, and IoT sensors, UPAJ offers region-specific, data-driven farming recommendations. The system has been positively received, with 92% user satisfaction, demonstrating its effectiveness in enhancing agricultural decision-making and productivity.

Conclusion

The paper introduces the Crop Suggestion, Disease Detection & Farmer Query Resolution System (UPAJ) as a holistic strategy to advance agricultural decision-making through machine learning mechanisms. Using the Random Forest Algorithm (RFA) to generate crop suggestion and Convolutional Neural Networks (CNN) for detecting disease, the system ensures farmers get proper and evidence-based suggestions. RFA effectively examines soil characteristics, climatic conditions, and past data to recommend the best crops, maximizing productivity and sustainability. CNN, however, facilitates accurate disease detection through image processing, enabling early diagnosis and proper treatment methodologies. The real-time query resolution system also helps in filling the gap between farmers and agricultural specialists to be able toensure timely and well-informed decision-making. The integration of these technologies not only improves crop yield but also reduces losses from poor crop choice and delayed disease control. Future development of this system may involve IoT-based real-time soil monitoring, improved deep learning architectures fordisease detection, and multi-language support for wider accessibility. Through the application of AI-based solutions, UPAJ helps bring about the modernization of agriculture, equipping farmers with smart tools to make them more efficient, minimize risks, and encourage sustainable farming methods.

References

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Copyright

Copyright © 2025 Purva Singh, Sanskrita Tewari, Ratan Rajan, Shivam Dixit. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.