Automated Sugarcane Disease Detection Using AI Driven Image Analysis
Ujjwal Singh1, Vishal Yadav2, Vishesh Kumar Singh3, Chinmay Shukla4
1UG Scholar, Dept. of CSE, Babu Banarasi Das Institute of Tech. & Mang., Lucknow, India
2UG Scholar, Dept. of CSE, Babu Banarasi Das Institute of Tech. & Mang., Lucknow, India
3UG Scholar, Dept. of CSE, Babu Banarasi Das Institute of Tech. & Mang., Lucknow, India
4Assistant professor, Dept. of CSE, Babu Banarasi Das Institute of Tech. & Mang., Lucknow, India
ABSTRACT
Sugarcane is one of the most important cash crops in countries like India, but its productivity is highly affected by various plant diseases such as red rot, leaf spot, and rust. Traditional disease detection methods rely on manual inspection, which is time-consuming, less accurate, and requires expert knowledge. To overcome these challenges, this research paper proposes an Artificial Intelligence-based approach for automatic sugarcane disease detection using image analysis.The system utilizes techniques from Artificial Intelligence and Computer Vision to analyze leaf images and identify disease patterns. A dataset of sugarcane leaf images is preprocessed using image enhancement and segmentation methods. Further, a Convolutional Neural Network (CNN) model is applied to classify the diseases with high accuracy.The proposed model aims to provide fast, reliable, and cost-effective disease detection, which can assist farmers in taking timely preventive measures. This approach not only improves crop yield but also reduces economic losses and supports smart agriculture practices. The results demonstrate that AI-driven systems can significantly enhance disease diagnosis efficiency compared to traditional methods.
Keywords: Sugarcane Disease Detection, Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Image Processing, Convolutional Neural Network (CNN), Leaf Disease Classification, Agricultural Technology, Smart Farming, Crop Health Monitoring, Pattern Recognition, Disease Prediction.
