Application of Lightweight Object Detection Network in Cucumber Leaf Detection

Li, Xia; Yue, Zhenchao; Su, Junhao; Wang, Sichao; Hua, Jiawei; Duan, Fangtao

doi:10.1109/icma54519.2022.9855974

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…A lightweight YOLOv4 model based on MobileNet v3 backbone with three tailored modules and transfer learning technique was developed for detection of cucumber leaves. The model attained mAP of 97.21% for detection of cucumber leaves [23]. A YOLOv5s based Apple-YOLO model is proposed including three customized modules for detection of apple leaf diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of YOLO Models in Plant Disease Detection

Ali,

Ismail,

Ariyaluran Habeeb

et al. 2024

JIWE

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Plant diseases significantly impact global agriculture, leading to substantial production losses and economic consequences. Timely disease detection can enhance crop yield, optimize resource utilization, reduce costs, and mitigate environmental effects, ultimately ensuring high-quality food production. Deep learning, specifically computer vision-based techniques, have proven invaluable in tasks like image classification, segmentation, and object detection. Deep Learning techniques such as You Only Look Once (YOLO) models are state of the art neural network algorithms used for accurate object detection. In this study, YOLOv5, YOLOv7 and YOLOv8 models were trained on CCL’20 dataset for citrus disease detection. Data augmentation techniques such as image translation, image scaling, flip, mosaic augmentations were implemented to improve the models’ performance during training phase. The model performance was evaluated using metric such as Mean Average Precision at 50% to 95% Intersection over Union score i.e. mAP@50-95. The results show that YOLOv8 model performs better than other variants and offers significant improvements over the benchmark performance from previous studies. The final hyper-parameter tuned model achieved 96.1% mAP@50-95 on testing data for citrus disease detection and mAP@50-95 of 95.3%, 96.0% and 97.0% for detection of Anthracnose, Melanose and Bacterial Brown Spot diseases, respectively. The trained model was able to detect single and multiple instances of same or different disease in an image showing the potential of recent YOLO models. The trained YOLOv8 model is deployed on Roboflow platform.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of YOLO Models in Plant Disease Detection

Ali,

Ismail,

Ariyaluran Habeeb

et al. 2024

JIWE

View full text Add to dashboard Cite

show abstract

“…The lush growth of cucumber vines and complex background attract many scholars to use the most advanced deep learning technologies to solve problems related to cucumber planting management. Li et al [1] proposed a lightweight object detection network for cucumber diseased leaves based on YOLOv4, which achieved mAP of 97.21% and FPS of 40.5, better balancing accuracy, speed, and computational complexity. Lou et al [2] improved the feature fusion network on the basis of YOLOv5-M version 3.0 with an mAP of 84.6%, which can effectively detect cucumber diseases.…”

Section: Introductionmentioning

confidence: 99%