International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Early detection of plant diseases is crucial for reducing economic losses and ensuring global food security. Traditional visual inspections by farmers are subjective and time-consuming, prompting the need for automated solutions. This paper presents a machine learning-based system for identifying and classifying plant leaf diseases using convolutional neural networks (CNNs). We describe the preprocessing, augmentation, and segmentation techniques employed to enhance data quality and improve model performance. Our experiments, conducted on a dataset of 90,000 images across 38 classes, achieved a training accuracy and a validation accuracy above 98% The system also features an intuitive web interface for practical deployment, supporting real-time detection in agricultural fields.

Keywords: Plant Disease Detection, Agriculture, Machine Learning, Deep Learning, Convolutional Neural Networks (CNN), Image Processing, Data Augmentation, Image Segmentation, Ensemble Learning, VGG16, VGG19, ResNet101V2, InceptionV3, LIME Explainability, Computer Vision, Transfer Learning, Attention Mechanisms, Public Datasets, Real-Time Detection, Web-Based System, Smart Agriculture, Food Security.

Downloads: | DOI: 10.17148/IJARCCE.2026.15141