Agriculture plays a vital role in ensuring global food security, but it is constantly threatened by plant diseases that can cause significant crop losses. Traditional methods of disease detection, such as manual inspection by experts, are time-consuming and not scalable, especially for large-scale farming. To address these challenges, the application of machine learning (ML) and computer vision has become an innovative solution for automating plant disease detection
Plant diseases are a major cause of agricultural loss worldwide. Crop diseases can lead to decreased yields, lower food quality, and financial losses for farmers. Early detection of diseases is crucial to prevent their spread and mitigate the damage. Traditional methods of plant disease detection typically involve manual inspection, which is time-consuming, prone to human error, and not scalable, especially for large-scale farming operations.
Recent advancements in machine learning and computer vision have provided innovative ways to automate the detection of plant diseases. Deep learning, especially Convolutional Neural Networks (CNNs), has proven to be highly effective for image-based classification tasks, making it a powerful tool for plant disease detection and classification.
This project focuses on utilizing deep learning techniques for the automatic detection and classification of plant diseases based on images of plant leaves. The project aims to create a robust model that can classify plant diseases into various categories, thus assisting farmers and agricultural experts in managing plant health efficiently
The project aims to develop a deep learning model capable of detecting and classifying plant diseases from leaf images. Given that many plant diseases look similar to one another, distinguishing them based on their visual appearance is a challenging task. This project seeks to address this challenge by training a machine learning model to classify diseases accurately.
• Detecting diseases in plants from images of their leaves. • Classifying the diseases into predefined categories such as "healthy," “Unhealthy,” "multiple diseases," “rust”, and "scab." • Evaluating the performance of different deep learning models (CNNs, transfer learning). • Deploying the model in an application that could assist in real-world farming practices for early disease detection
The scope of this project focuses on identifying a limited set of plant diseases for specific crops, such as Areca, Paddy, Sugarcane and dataset of Plant Pathology 2020 - FGVC7. The model will classify images into multiple classes such as healthy, unhealthy and other common diseases affecting these crops.