Improvements in the automated disease detection and analysis areas may provide important benefits for early action that would allow intervention at earlier stages for cure and preventing spread of the disease. As a result, damages on crop yield could be minimized. This study p...
Figure1shows the general methodology of CNN applications. This review focuses on several key aspects of CNN applications in potato disease detection: (1) the types and characteristics of datasets used for training and testing models; (2) preprocessing techniques applied to image data; (3) the geo...
Potato is one of the most cultivated and in-demand crops after rice and wheat. Potato farming dominates as an occupation in the agriculture domain in more than 125 countries. However, even these crops are, subjected to infections and diseases, mostly cat
prevent overfitting; potato leaves were extracted by YOLO v5 image segmentation and labelled with LabelMe for building data samples; the component modules of the YOLO v5 network were replaced using model compression technology (ActNN) for potato disease detection when the device is low on memory. ...
Deep Learning Approaches for Potato Leaf Disease Detection: Evaluating the Efficacy of Convolutional Neural Network Architectures In agriculture, timely and accurate detection of plant diseases is essential to obtain healthy crop yields and ensure food security. However, detecting dis... Erlin,I Fuadi,...
www.nature.com/scientificreports OPEN received: 10 June 2016 accepted: 07 September 2016 Published: 27 September 2016 Fabrication of potato-like silver molybdate microstructures for photocatalytic degradation of chronic toxicity ciprofloxacin and highly selective electrochemical detection of H2O2 J. Vinoth...
Comparing inception V3, vgg 16, vgg 19, CNN, and ResNet 50: A case study on early detection of a rice disease Agronomy, 13 (6) (2023), p. 1633, 10.3390/agronomy13061633 View in ScopusGoogle Scholar Simonyan and Zisserman, 2014 K. Simonyan, A. Zisserman Very deep convolutional vetwork...
Utilizing the power of computer vision and deep learning, this paper presents a comprehensive study on potato leaf disease detection using a multi-architecture Convolutional Neural Networks (CNNs) approach. We evaluate five different CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50...
In a field study, models with healthy leaves and five progressive disease stages were trained under laboratory conditions. The model developed was then applied under real field conditions. The authors highlighted the difficulties of using laboratory data to train field disease detection models (Appelta...
This study proposes a highly efficient CNN (convolutional neural network) architecture that is suitable for potato disease detection. A database is created for the training set using image processing. Adam is used as the optimizer and cross-entropy is used for model analysis. Softmax is used as...