To address such issues, this paper proposes plants leaf disease detection and preventive measures technique in the agricultural field using image processing and two well-known convolutional neural network (CNN) models as AlexNet and ResNet-50. Firstly, this technique is applied on Kaggle datasets of...
The classification of healthy and diseased citrus leaf images using a (CNN) on the Platform as a Service (PaaS) cloud has been developed. The method has been tested using pre-trained backbones and proposed CNN, and attained 98.0% accuracy and 99.0% F1-score39. A modified transfer learning ...
According to [4] Plant leaf disease is a major issue in rice production, and the disease has the potential to harm the crop, resulting in a drop in products. Farmers have a difficult time detecting and classifying plant leaf diseases. The traditional method of detecting and classifying diseases...
This literature review aims to examine the use of deep learning techniques for detecting plant diseases through plant leaf analysis. We reviewed 160 articles published between 2020 and 2024 from trusted academic sources such as IEEE Xplore, Springer, Google Scholar, and SCOPUS. Using specific keywords...
Panigrahi KP, Das H, Sahoo AK et al (2020) Maize leaf disease detection and classification using machine learning algorithms. In: Progress in computing, analytics and networking: proceedings of ICCAN 2019, Springer, pp 659–669 Patil RR, Kumar S, Rani R (2022) Comparison of artificial intelli...
Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot Corn_(maize)__Common_rust Corn_(maize)___Northern_Leaf_Blight Corn_(maize)___healthy Grape___Black_rot Grape___Esca_(Black_Measles) Grape___Leaf_blight_(Isariopsis_Leaf_Spot) ...
Harakannanavar SS, Rudagi JM, Puranikmath VI, Siddiqua A, Pramodhini R (2022) Plant leaf disease detection using computer vision and machine learning algorithms. Global Transitions Proceedings 3(1):305–310 Article Google Scholar Zhang J, Tian Y, Yan L, Wang B, Wang L, Xu J, Wu K ...
The authors demonstrated that using the SVM classifier on rice leaf disease classification could categorize eleven deep CNN model features and obtain an average of 98.38% using ResNet50 depth SVM [1]. Authors in [30] identified ten diseases of fourplant speciesusing six pretrained TL architectures...
we need a model that doesn’t need much pre-processing and doesn’t need manual functional (feature) extraction. So, in this study, methods for identifying and classifying plant diseases from leaf images taken at different resolutions are described that are based on deep learning. Using Deep Co...
The real-time crop disease diagnosis is based on a convolutional neural network (CNN) that was trained, validated, and tested on a dataset of 87,860 leaf images split into 38 classes. To design an optimal CNN, 16 different CNNs were designed and tested. MobileNetV2 using the Canny Edge ...