Thursday 20 March 2025
The humble potato, a staple food for millions around the world, is facing a new threat. Late blight, a fungal disease that can decimate entire crops in a matter of weeks, has been affecting potato yields globally. But scientists may have found a way to detect and diagnose this disease more accurately using deep learning algorithms.
Researchers from various institutions have been working together to develop a system that uses image processing and machine learning to identify late blight on potato leaves. The approach involves preprocessing images of potato leaves, extracting features from the images using convolutional neural networks (CNNs), and then selecting the most relevant features for classification.
The team used a dataset of 1500 images of healthy and diseased potato leaves to train their model. They found that by combining features extracted from different CNN models, they could achieve an accuracy rate of over 99% in identifying late blight. This is significantly higher than the 85% accuracy rate achieved by human experts.
The system works by first resizing the images to a standard size and then enhancing them using histogram equalization. The images are then fed into a series of CNN models, including Darknet-53, AlexNet, and Vgg-19. These models extract features from the images, such as edges, textures, and shapes.
The features extracted by each model are then concatenated and used to train an SVM (support vector machine) classifier. The classifier is trained on a subset of the dataset and can accurately identify late blight based on the features it has learned.
One of the key advantages of this system is its ability to detect early stages of the disease, when symptoms are still subtle. This allows farmers to take action earlier, reducing the risk of crop loss and minimizing the need for pesticides and other chemicals.
The researchers hope that their system will be integrated into existing farming practices, allowing farmers to more accurately diagnose late blight and make data-driven decisions about how to manage their crops. The potential benefits are significant: improved yields, reduced waste, and a more sustainable food supply chain.
In addition to its practical applications, this research highlights the power of deep learning in solving real-world problems. By combining machine learning with computer vision, researchers can develop systems that can analyze complex images and make accurate predictions about what they contain.
The potential for this technology extends beyond agriculture, however. It could be applied to a wide range of fields where image analysis is critical, such as medical diagnosis, quality control in manufacturing, or even autonomous vehicles.
Cite this article: “AI-Powered System Accurately Detects Late Blight Disease in Potatoes”, The Science Archive, 2025.
Potato, Late Blight, Deep Learning, Image Processing, Machine Learning, Convolutional Neural Networks, Cnns, Support Vector Machine, Svm, Crop Management
