The potato leaf classification accuracy of the proposed model is 97.12%. The proposed CNN model also provides an accuracy of 98.62% while identifying late blight disease. The ten-fold cross-validation technique is used to observe the performance of the proposed late blight classifier and then ...
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 ...
Inference time is important to check the real-time approach of robots for object detection using CNNs (Redmon and Angelova, 2015). There are two steps to develop an automated system for detection purposes: first, the training of a DCNN model by using the available dataset, usually on a GPU...
(ii) Late blight. Moreover, these diseases lead to damage the crop and decreases its production. In this paper, we propose a deep learning-based approach to detect the early and late blight diseases in potato by analyzing the visual interpretation of the leaf of several potato crops. The ...
Gupta U, Vijh S, Kumar S et al (2023c) Potato Leaf Disease Detection Using Machine Learning Techniques for Precision Agriculture. In: Proc. IEEE Int. Conf. Image Inf. Process., ICIIP. Institute of Electrical and Electronics Engineers Inc., pp 913–918 ...
This study proposes a new deep-learning model that correctly classifies plant leaf diseases for the agriculture and food sectors. It focuses on the detection of plant diseases for potato leaves from images by designing a new convolutional neural network (CNN) architecture. The CNN methodology ...
leaf.The Mask R-CNN model was able to correctly differentiate between the diseased patch on the potato leaf and the similar-looking background soil patches,which can confound the outcome of binary classification.To improve the detection performance,the original RGB dataset was then converted to HSL...
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...
During the comparison analysis paper, the intended model was able to accurately determine and detect diseases in potato leaf stands using CNN which includes ResNet algorithm and UNet model which comes under deep learning methods. We tried both machine learning (SVM) and deep learning model (Res...
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 ...