Results 1 to 10 of about 7,991,343 (226)

MGA-YOLO: A lightweight one-stage network for apple leaf disease detection. [PDF]

Front Plant Sci, 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
Wang Y, Wang Y, Zhao J.
europepmc   +4 more sources

YOLO-ACT: an adaptive cross-layer integration method for apple leaf disease detection. [PDF]

Front Plant Sci
Apple is a significant economic crop in China, and leaf diseases represent a major challenge to its growth and yield. To enhance the efficiency of disease detection, this paper proposes an Adaptive Cross-layer Integration Method for apple leaf disease ...
Zhang S, Wang J, Yang K, Guan M.
europepmc   +4 more sources

A High-Precision Detection Method of Apple Leaf Diseases Using Improved Faster R-CNN

Agriculture, 2023
Apple leaf diseases seriously affect the sustainable production of apple fruit. Early infection monitoring of apple leaves and timely disease control measures are the key to ensuring the regular growth of apple fruits and achieving a high-efficiency ...
Xulu Gong, Shujuan Zhang
doaj   +4 more sources

An improved YOLOv5-based apple leaf disease detection method. [PDF]

Sci Rep
The effective identification of fruit tree leaf disease is of great practical significance to reduce pesticide spraying, improve fruit yield and realize ecological agriculture.
Liu Z, Li X.
europepmc   +3 more sources

YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection. [PDF]

Front Plant Sci, 2023
Apple trees face various challenges during cultivation. Apple leaves, as the key part of the apple tree for photosynthesis, occupy most of the area of the tree.
Lv M, Su WH.
europepmc   +2 more sources

Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

IEEE Access, 2019
Alternaria leaf spot, Brown spot, Mosaic, Grey spot, and Rust are five common types of apple leaf diseases that severely affect apple yield. However, the existing research lacks an accurate and fast detector of apple diseases for ensuring the healthy ...
Peng Jiang, Yuehan Chen, Bin Liu, Dongjian He, Chunquan Liang   +4 more
doaj   +3 more sources

EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5

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, Wanli Ma, Jianlong Wang, Meijuan Yang, Yongmao Wang, Chunyang Wang   +5 more
doaj   +3 more sources

SRC-YOLOv8n: a lightweight framework for fine-grained apple leaf disease detection with spatial detail preservation and multi-scale feature enhancement. [PDF]

Front Plant Sci
Apple leaf disease detection is crucial for maintaining crop health and ensuring food security, yet current detection methods face significant challenges in balancing accuracy with computational efficiency.
Cui H, Song C, Zhong C, Du P, Xie L, Song Y, Li R, Jing X, Ouyang Q.   +8 more
europepmc   +2 more sources

Edge-enhanced dual branch CNN with adaptive attention for robust apple leaf disease detection. [PDF]

BMC Plant Biol
Accurate detection of apple leaf diseases remains a critical challenge in precision agriculture, where complex field conditions and subtle symptom variations often degrade model performance.
Shahade AK, Deshmukh PV.
europepmc   +2 more sources

A Novel Approach for Classification and Detection of Apple Leaf Disease Using Enhanced RBVT-Net With Transfer Learning and YoloV7

IEEE Access
Apples are a popular fruit worldwide, valued for their rich nutritional content and associated health benefits, such as reducing the risks for cancer, diabetes, and heart disease.
Satish Kumar, Rakesh Kumar, Meenu Gupta, Korhan Cengiz, Nikola Ivkovic   +4 more
doaj   +3 more sources