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
In this research, we suggest the hybridization of the Greylag Goose Optimizer (GGO) with the Grey Wolf Optimizer (GWO), which is called GGGWO, for the optimization of convolutional neural network (CNN) models for potato disease classification. Through our approach, we are seeking to enhance ...
PSTVd-infected tubers may be small, elongated, from which the disease derives its name, misshapen, and cracked. Viroids, the first known representatives of a new domain of “subviral pathogens,” stand out in many respects. First,viroidsare the only infectious agents that lack protein component...
[19] proposed to train the Faster R-CNN model using a transfer learning approach and to mark out patch regions. The K-means algorithm clustered the established colour and the SIFT features and then passed them into SVM for disease classification with an average accuracy of 90.83%. Although ...
The classification tasks are performed using a global average pooling layer and a fully connected layer. The model was trained, validated, and tested on custom datasets specifically curated for potato leaf disease detection. EfficientRMT-Net's performance was compared with other deep learning and ...
2.5.2. CNN models Three one-dimensional deep CNNs i.e. VGG-19, InceptionV3, and SpectraNet-32 were developed using the Keras API in Tensorflow. Before going into the details of their architecture, a brief overview of the data preprocessing and hyperparameters is given. As depicted in Fig....
The CNN methodology applies filters to input images, extracts key features, reduces dimensions while preserving important characteristics in images, and finally, performs classification. The experimental results conducted on a real-world dataset showed that a significant improvement (8...
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...
Singha A, Hossain Moon MS, Roy Dipta S (2023) An End-to-End Deep Learning Method for Potato Blight Disease Classification Using CNN. In: Int. Conf. Comput. Intell., Networks Secur., ICCINS. Institute of Electrical and Electronics Engineers Inc. ...
An accuracy of 88% was achieved in the classification of healthy and affected potato leaves. 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 ...