A Lightweight YOLOv8 Model for Apple Leaf Disease Detection
Applied SciencesChina holds the top position globally in apple production and consumption. Detecting diseases during the planting process is crucial for increasing yields and promoting the rapid development of the apple industry.
Lijun Gao +6 more
doaj +4 more sources
An Improved Lightweight Network for Real-Time Detection of Apple Leaf Diseases in Natural Scenes
Agronomy, 2022 Achieving rapid and accurate detection of apple leaf diseases in the natural environment is essential for the growth of apple plants and the development of the apple industry.
Sha Liu +5 more
doaj +2 more sources
Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases
Plants, 2022 Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and ...
Ruilin Zhu +3 more
doaj +2 more sources
ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases [PDF]
Frontiers in Plant Science, 2023 Suffering from various apple leaf diseases, timely preventive measures are necessary to take. Currently, manual disease discrimination has high workloads, while automated disease detection algorithms face the trade-off between detection accuracy and ...
Weishi Xu, Runjie Wang
doaj +2 more sources
Multidimensional Attention-Based CNN Model for Identifying Apple Leaf Disease
Journal of Food Quality, 2023 To prevent the spread of illnesses and guarantee the steady and healthy growth of the apple sector, the proper diagnosis of apple leaf diseases is of utmost importance.
Kahkashan Perveen +7 more
doaj +2 more sources
Diagnosis and Mobile Application of Apple Leaf Disease Degree Based on a Small-Sample Dataset
Plants, 2023 The accurate segmentation of apple leaf disease spots is the key to identifying the classification of apple leaf diseases and disease severity. Therefore, a DeepLabV3+ semantic segmentation network model with an actors spatial pyramid pool module (ASPP ...
Lili Li, Bin Wang, Yanwen Li, Hua Yang
doaj +2 more sources
Real-Time Detection of Apple Leaf Diseases in Natural Scenes Based on YOLOv5
Agriculture, 2023 Aiming at the problem of accurately locating and identifying multi-scale and differently shaped apple leaf diseases from a complex background in natural scenes, this study proposed an apple leaf disease detection method based on an improved YOLOv5s model.
Huishan Li, Lei Shi, Siwen Fang, Fei Yin
doaj +2 more sources
CEFW-YOLO: A High-Precision Model for Plant Leaf Disease Detection in Natural Environments
AgricultureThe accurate and rapid detection of apple leaf diseases is a critical component of precision management in apple orchards. The existing deep-learning-based detection algorithms for apple leaf diseases typically demand high computational resources, which ...
Jinxian Tao +3 more
doaj +2 more sources
Semantic-Decoupled Dual Attention Network for Robust Apple Disease Detection
AgricultureApple leaf disease detection in orchards faces a unique challenge: lesion regions (foreground) are semantically important but visually less salient (weak texture, blurred boundaries), while background distractors (soil, weeds) are visually salient but ...
Mengyu Liu +6 more
doaj +2 more sources
Detection of Apple Leaf Gray Spot Disease Based on Improved YOLOv8 Network
MathematicsIn the realm of apple cultivation, the efficient and real-time monitoring of Gray Leaf Spot is the foundation of the effective management of pest control, reducing pesticide dependence and easing the burden on the environment.
Siyi Zhou +4 more
doaj +3 more sources