AI-Driven Corn Disease Detection and Recommendation System Using Machine Learning and Deep Learning

Abstract

Corn is one of the most harvested crops. However, corn leaf diseases can reduce yield and damage its growth, which can lead to financial losses for farmers. Early detection of these diseases must be a top priority. This article uses leaf images on an ai-based system to identify disease and provide pesticide recommendation using machine learning and deep learning models. A Convolutional Neural Network (CNN) model is used to analyze leaf images and correctly classify the disease. As a result, the system provides the pesticide recommendation based on the disease detected. The Ai-driven approach helps farmers improve yield quality and make better decisions.

Introduction

Corn is a globally important crop, but diseases like leaf blight, rust, and gray leaf spot threaten its yield and quality. Traditional manual inspection for these diseases is time-consuming, labor-intensive, and error-prone. To address this, an AI-based system using machine learning (ML) and deep learning (DL)—especially convolutional neural networks (CNN)—has been developed to automatically detect corn leaf diseases from images and recommend appropriate treatments such as pesticides or fertilizers.

The system collects and preprocesses images of corn leaves, trains various ML algorithms (SVM, Random Forest, Decision Tree, Logistic Regression, KNN) alongside deep learning CNN models, and evaluates them for accuracy. CNN models generally outperform traditional ML models in disease classification accuracy. The AI solution enables early disease detection, reducing crop losses and improving agricultural productivity. IoT and cloud technologies enhance real-time monitoring and data processing, promoting precision agriculture.

The developed system includes a web application where farmers can upload leaf images for disease diagnosis and receive treatment recommendations. Visualization tools like confusion matrices and feature maps demonstrate the model’s effectiveness in distinguishing diseases. This technology helps farmers manage crop health efficiently, conserve resources, and increase yields, thereby supporting global food security.

Conclusion

The corn disease detection and recommendation system offers a reliable and efficient way of recognizing traditional corn leaves and recommendations of suitable treatment. This technology uses high-end machine learning and deep learning algorithms to accurately classify the pictures of leaf as healthy or harmful. It also provides recommendations that help farmers in implementing rapid measures. The findings show a remarkable improvement in the detection of the disease, reduce crop loss. Future improvements that include real-time training and monitoring into greater diversity of data files could significantly improve the accuracy of the model. This method creates higher yields and healthier crops, making it a valuable tool for contemporary agriculture.

References

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Copyright

Copyright © 2025 Sneha Barve, Shravani Akolkar, Yashodeep Bhamare, Gaurav Borawake, B. J. Dange. 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.