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...
Using CNNs for potato disease detection involves taking pictures of plants and feeding them into a computer program that has been trained to recognize specific patterns linked to diseases. This technology not only helps farmers save time and effort but also improves crop health and reduces the use...
(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 ...
With the rapid development of artificial intelligence to promote precision agriculture, artificial intelligence (AI), machine learning (ML), and computer vision (CV) technologies are used for automatic crop leaf disease detection [6–8], which are time-sensitive and efficient and requires less human...
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 ...
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...
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...
A novel framework for potato leaf disease detection using an efficient deep learning model Potato disease management plays a valuable role in the agriculture field as it might cause a significant loss in crops production. Therefore, timely recogn... R Mahum,H Munir,ZUN Mughal,... - 《Human &...
Using only 6 wavelengths, the potato leaves were classified at a stage where no symptoms were yet visible (Qi et al. 2023). In a recent study, a novel structure, Atrous-CNN (Convolutional Neural Networks) was developed to classify different potato leaf diseases (Anthrax, leaf blight, early ...
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...