Plant image recognition with deep learning: A review
Y Chen, Y Huang, Z Zhang, Z Wang, B Liu, C Liu… - … and Electronics in …, 2023 - Elsevier
Significant advances in the field of digital image processing have been achieved in recent
years using deep learning, which has significantly exceeded previous methods. Deep …
years using deep learning, which has significantly exceeded previous methods. Deep …
Applications of deep-learning approaches in horticultural research: a review
B Yang, Y Xu - Horticulture Research, 2021 - academic.oup.com
Deep learning is known as a promising multifunctional tool for processing images and other
big data. By assimilating large amounts of heterogeneous data, deep-learning technology …
big data. By assimilating large amounts of heterogeneous data, deep-learning technology …
A CNN-SVM study based on selected deep features for grapevine leaves classification
The main product of grapevines is grapes that are consumed fresh or processed. In addition,
grapevine leaves are harvested once a year as a by-product. The species of grapevine …
grapevine leaves are harvested once a year as a by-product. The species of grapevine …
Grape disease image classification based on lightweight convolution neural networks and channelwise attention
Z Tang, J Yang, Z Li, F Qi - Computers and Electronics in Agriculture, 2020 - Elsevier
In this paper, a lightweight convolution neural network model is proposed to diagnose grape
diseases, including black rot, black measles and leaf blight. Focusing on small and low …
diseases, including black rot, black measles and leaf blight. Focusing on small and low …
[HTML][HTML] A hybrid model of ghost-convolution enlightened transformer for effective diagnosis of grape leaf disease and pest
X Lu, R Yang, J Zhou, J Jiao, F Liu, Y Liu, B Su… - Journal of King Saud …, 2022 - Elsevier
Disease and pest are the main factors causing grape yield reduction. Correct and timely
identification of these symptoms are necessary for the vineyard. However, the commonly …
identification of these symptoms are necessary for the vineyard. However, the commonly …
[HTML][HTML] SLViT: Shuffle-convolution-based lightweight Vision transformer for effective diagnosis of sugarcane leaf diseases
Farmers must accurately and promptly identify sugarcane leaf diseases with identical
symptoms. RGB images have a beneficial function in disease identification. Nevertheless …
symptoms. RGB images have a beneficial function in disease identification. Nevertheless …
Automatic fish population counting by machine vision and a hybrid deep neural network model
Simple Summary In aquaculture, the number of fish population can provide valuable input
for the development of an intelligent production management system. Therefore, by using …
for the development of an intelligent production management system. Therefore, by using …
Using EfficientNet and transfer learning for image-based diagnosis of nutrient deficiencies
Early diagnosis of nutrient deficiencies can play a major role in avoiding significant
agricultural losses and increasing the final yield while preserving the environment through …
agricultural losses and increasing the final yield while preserving the environment through …
Deep neural networks for grape bunch segmentation in natural images from a consumer-grade camera
Precision agriculture relies on the availability of accurate knowledge of crop phenotypic
traits at the sub-field level. While visual inspection by human experts has been traditionally …
traits at the sub-field level. While visual inspection by human experts has been traditionally …
Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks
In recent years, many efforts have been made to apply image processing techniques for
plant leaf identification. However, categorizing leaf images at the cultivar/variety level …
plant leaf identification. However, categorizing leaf images at the cultivar/variety level …