Diagnosis and Mobile Application of Apple Leaf Disease Degree Based on a Small-Sample Dataset [PDF]
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
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Identification of apple leaf disease via novel attention mechanism based convolutional neural network [PDF]
Frontiers in Plant Science, 2023 IntroductionThe identification of apple leaf diseases is crucial for apple production.MethodsTo assist farmers in promptly recognizing leaf diseases in apple trees, we propose a novel attention mechanism. Building upon this mechanism and MobileNet v3, we
Hebin Cheng, Heming Li
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HSSNet: A End-to-End Network for Detecting Tiny Targets of Apple Leaf Diseases in Complex Backgrounds [PDF]
Plants, 2023 Apple leaf diseases are one of the most important factors that reduce apple quality and yield. The object detection technology based on deep learning can detect diseases in a timely manner and help automate disease control, thereby reducing economic ...
Xing Gao +5 more
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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
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MGA-YOLO: A lightweight one-stage network for apple leaf disease detection [PDF]
Frontiers in Plant Science, 2022 Apple leaf diseases seriously damage the yield and quality of apples. Current apple leaf disease diagnosis methods primarily rely on human visual inspection, which often results in low efficiency and insufficient accuracy. Many computer vision algorithms
Yiwen Wang, Yaojun Wang, Jingbo Zhao
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EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5 [PDF]
Frontiers in Plant Science, 2023 IntroductionCurrent detection methods for apple leaf diseases still suffer some challenges, such as the high number of parameters, low detection speed and poor detection performance for small dense spots, which limit the practical applications in ...
Shisong Zhu +5 more
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A method of detecting apple leaf diseases based on improved convolutional neural network. [PDF]
PLoS ONE, 2022 Apple tree diseases have perplexed orchard farmers for several years. At present, numerous studies have investigated deep learning for fruit and vegetable crop disease detection.
Jie Di, Qing Li
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AppleLeafNet: a lightweight and efficient deep learning framework for diagnosing apple leaf diseases [PDF]
Frontiers in Plant ScienceAccurately identifying apple diseases is essential to control their spread and support the industry. Timely and precise detection is crucial for managing the spread of diseases, thereby improving the production and quality of apples.
Muhammad Umair Ali +5 more
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Apple leaf disease severity grading based on deep learning and the DRL-Watershed algorithm [PDF]
Scientific ReportsApple leaf diseases significantly impair the photosynthetic efficiency and growth quality of apple trees, leading to reduced fruit yields. Existing methods for disease detection and severity classification struggle to quickly and accurately segment and ...
Zhifang Bi +7 more
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Apple chlorotic leaf spot virus [PDF]
, 2016 NYS IPM Type: Fruits IPM Fact SheetApple chlorotic leaf spot virus (ACLSV) infects pome and stone fruits. It can elicit diverse symptoms although, in most cultivars the virus is latent, which means that infected trees do not manifest observable symptoms.
Cieniewicz, Elizabeth, Fuchs, Marc
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