Nov 25, 2021 · We propose a deep learning-based feature extraction method for the identification of plant species and the classification of plant leaf diseases.
This paper studies the use of deep-learning models (AlexNet, VggNet, ResNet) pre-trained on object categories (ImageNet) in applied texture classification ...
Dec 9, 2021 · ABSTRACT This paper studies the use of deep-learning models (AlexNet, VggNet, ResNet) pre-trained on object categories (ImageNet) in applied ...
Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture.
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Jan 22, 2024 · We developed a convolutional neural network (CNN)-based framework for identifying pest-borne diseases in tomato leaves using the Plant Village Dataset and the ...
Oct 22, 2024 · This paper presents a comprehensive survey of deep learning approaches for crop disease analysis in precision agriculture. The investigation ...
A robust CNN module which can successfully recognize and classify the dissimilar leaf health conditions of non-identical plants from the in-field RGB images.
This study offers a remedy for accurately diagnosing and classifying rice leaf diseases through deep learning techniques.
Oct 18, 2023 · This study will provide a survey in different aspects of the topic including data, techniques, and applications.
An optimized dense convolutional neural network (CNN) architecture (DenseNet) for corn leaf disease recognition and classification is proposed in this paper.