Potato disease classification

Description: This research focuses on developing an efficient machine learning-based system for detecting diseases in potato crops. Leveraging image data, the model aims to classify and identify various potato diseases accurately, helping farmers manage crop health and improve yield. Using the Disease potato 1500 and Healthy potato 1527 datasets, the research explores robust data processing, augmentation techniques, and advanced machine learning algorithms for precise disease diagnosis.

Dataset Overview:
1.Disease potato 1500: 1,500 images of diseased potato.
2.Healthy potato 1527: 1,527 images of healthy potatoes.

Key Features:
1.Number of Images: 3,027 total images (1,500 in Disease potato, 1,527 in Healthy potato).
2.Number of Augmented Images:Potentially expanded dataset through augmentation (rotation, scaling, flipping, etc.).
3.File Formats: Images available in JPEG format.
4.Disease Type: Different diseases such as late blight, early blight, and other fungal infections affecting potato crops.

Applications:
1.Disease Classification: Detect and classify potato diseases using supervised learning algorithms like Convolutional Neural Networks (CNNs).
2.Yield Optimization: Assist farmers by providing timely disease detection to optimize crop yield.
3.Agricultural Automation: Integrate with drone or smartphone-based applications for real-time field analysis.

Dataset Collection Procedure:
The dataset was collected through a combination of field observations and controlled experiments.Images were then labeled according to disease type and stage, providing a comprehensive dataset for training machine learning models. The dataset also underwent augmentation to enhance its diversity and robustness, ensuring the model can generalize well in real-world applications.