Year: 2025
Type: article
Abstract: : Potatoes are widely grown worldwide and play a significant role in many cuisines, especially in India, where they are among the top crops cultivated. Potato plants often get sick, which can make them not very good to eat and make fewer potatoes grow. For maximum production it is necessary to maintain crop health by identifying various kinds of diseases. Similar to approaches used to identify illnesses in tomato leaves, advancements in deep learning and machine learning techniques appear potential for automating the identification and treatment of potato issues. These approaches involve steps like preparing datasets, processing images, extracting features, and training models. Various algorithms and models like VGG-16, U-Net, Inception-v3, PLeaD-Net, and PLDPNet have been utilized, demonstrating high accuracies in recognizing disease patterns in plant images. This improvement in detecting potato plant diseases helps farmers take better care of their crops and find ways to make them healthier and produce more potatoes.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4409602241