U3-YOLOXs: An improved YOLOXs for Uncommon Unregular Unbalance detection of the rape subhealth regions

Gong, Xinjing; Zhang, Xihai; Zhang, Ruwen; Wu, Qiufeng; Wang, Hao; Guo, R.T.; Chen, Zerui

doi:10.1016/j.compag.2022.107461

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…The use of focal-EIOU loss in the loss function can boost the weight of high-quality bounding boxes, suppress the weight of low-quality bounding boxes, and solve the problem of imbalance between difficult and easy samples. Similar conclusions have been reached in related studies [31,32].…”

Section: Discussionsupporting

confidence: 92%

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Wang,

Han

et al. 2024

Agriculture

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As strawberries are a widely grown cash crop, the development of strawberry fruit-picking robots for an intelligent harvesting system should match the rapid development of strawberry cultivation technology. Ripeness identification is a key step to realizing selective harvesting by strawberry fruit-picking robots. Therefore, this study proposes combining deep learning and image processing for target detection and classification of ripe strawberries. First, the YOLOv8+ model is proposed for identifying ripe and unripe strawberries and extracting ripe strawberry targets in images. The ECA attention mechanism is added to the backbone network of YOLOv8+ to improve the performance of the model, and Focal-EIOU loss is used in loss function to solve the problem of imbalance between easy- and difficult-to-classify samples. Second, the centerline of the ripe strawberries is extracted, and the red pixels in the centerline of the ripe strawberries are counted according to the H-channel of their hue, saturation, and value (HSV). The percentage of red pixels in the centerline is calculated as a new parameter to quantify ripeness, and the ripe strawberries are classified as either fully ripe strawberries or not fully ripe strawberries. The results show that the improved YOLOv8+ model can accurately and comprehensively identify whether the strawberries are ripe or not, and the mAP50 curve steadily increases and converges to a relatively high value, with an accuracy of 97.81%, a recall of 96.36%, and an F1 score of 97.07. The accuracy of the image processing method for classifying ripe strawberries was 91.91%, FPR was 5.03%, and FNR was 14.28%. This study demonstrates the program’s ability to quickly and accurately identify strawberries at different stages of ripeness in a facility environment, which can provide guidance for selective picking by subsequent fruit-picking robots.

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Section: Discussionsupporting

confidence: 92%

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Wang,

Han

et al. 2024

Agriculture

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“…Currently, self-supervised learning in agriculture is increasingly favored [13][14][15], and its learning advantages and good performance in classification tasks are exactly what is needed for plant leaf disease recognition [16]. Self-supervised learning does not require annotation in the traditional sense [17].…”

Section: Introductionmentioning

confidence: 99%

Classification of Plant Leaf Disease Recognition Based on Self-Supervised Learning

Wang,

Yin,

et al. 2024

Agronomy

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Accurate identification of plant diseases is a critical task in agricultural production. The existing deep learning crop disease recognition methods require a large number of labeled images for training, limiting the implementation of large-scale detection. To overcome this limitation, this study explores the application of self-supervised learning (SSL) in plant disease recognition. We propose a new model that combines a masked autoencoder (MAE) and a convolutional block attention module (CBAM) to alleviate the harsh requirements of large amounts of labeled data. The performance of the model was validated on the CCMT dataset and our collected dataset. The results show that the improved model achieves an accuracy of 95.35% and 99.61%, recall of 96.2% and 98.51%, and F1 values of 95.52% and 98.62% on the CCMT dataset and our collected dataset, respectively. Compared with ResNet50, ViT, and MAE, the accuracies on the CCMT dataset improved by 1.2%, 0.7%, and 0.8%, respectively, and the accuracy of our collected dataset improved by 1.3%, 1.6%, and 0.6%, respectively. Through experiments on 21 leaf diseases (early blight, late blight, leaf blight, leaf spot, etc.) of five crops, namely, potato, maize, tomato, cashew, and cassava, our model achieved accurate and rapid detection of plant disease categories. This study provides a reference for research work and engineering applications in crop disease detection.

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Research on Real-time Detection of Stacked Objects Based on Deep Learning

Geng,

Qiao,

Liu

et al. 2023

J Intell Robot Syst

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U3-YOLOXs: An improved YOLOXs for Uncommon Unregular Unbalance detection of the rape subhealth regions

Cited by 7 publications

References 16 publications

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Classification of Plant Leaf Disease Recognition Based on Self-Supervised Learning

Research on Real-time Detection of Stacked Objects Based on Deep Learning

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