Pitaya detection in orchards using the MobileNet-YOLO model

Li, Xiuli; Qin, Yi; Wang, Fujie; Guo, Feng; Yeow, John T. W.

doi:10.23919/ccc50068.2020.9189186

Cited by 23 publications

(18 citation statements)

References 12 publications

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“…YOLO models were applied in numerous applications where fast detection was needed, such as pedestrian detection [37], license plate recognition [38], and automatic detection of fabric defects [39]. In agriculture, YOLO application ranges from fruit detection [40][41][42], crop disease identification [43,44], and weed and pest identification [45,46]. The YOLO's fruit detection application was mainly based on apple orchards, and application in vine bunch detection is still missing.…”

Section: Yolo (You Only Look Once) and Frameworkmentioning

confidence: 99%

“…According to the presented features, YOLOv3, YOLOv4, and YOLOv5 models are characterised by a different dimension (number of layers and complexity), average accuracy, speed of detection, and training. Numerous applications of YOLO were proposed for fruit detection [42][43][44][45], but a limited number of studies investigated on the YOLO application for grapevine bunch detection [35]. In the current study, a comparison of YOLOv3, YOLOv4, and YOLOv5 models for automatic bunch detection in white grapevine was carried out.…”

Section: Yolov5mentioning

confidence: 99%

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Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms

et al. 2022

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Over the last few years, several Convolutional Neural Networks for object detection have been proposed, characterised by different accuracy and speed. In viticulture, yield estimation and prediction is used for efficient crop management, taking advantage of precision viticulture techniques. Convolutional Neural Networks for object detection represent an alternative methodology for grape yield estimation, which usually relies on manual harvesting of sample plants. In this paper, six versions of the You Only Look Once (YOLO) object detection algorithm (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv5x, and YOLOv5s) were evaluated for real-time bunch detection and counting in grapes. White grape varieties were chosen for this study, as the identification of white berries on a leaf background is trickier than red berries. YOLO models were trained using a heterogeneous dataset populated by images retrieved from open datasets and acquired on the field in several illumination conditions, background, and growth stages. Results have shown that YOLOv5x and YOLOv4 achieved an F1-score of 0.76 and 0.77, respectively, with a detection speed of 31 and 32 FPS. Differently, YOLO5s and YOLOv4-tiny achieved an F1-score of 0.76 and 0.69, respectively, with a detection speed of 61 and 196 FPS. The final YOLOv5x model for bunch number, obtained considering bunch occlusion, was able to estimate the number of bunches per plant with an average error of 13.3% per vine. The best combination of accuracy and speed was achieved by YOLOv4-tiny, which should be considered for real-time grape yield estimation, while YOLOv3 was affected by a False Positive–False Negative compensation, which decreased the RMSE.

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Section: Yolo (You Only Look Once) and Frameworkmentioning

confidence: 99%

Section: Yolov5mentioning

confidence: 99%

Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms

et al. 2022

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“…The Focal loss focused on hard-to-classify samples during the training process without affecting the original detection speed. Formula (7) of this function is as follows (Li et al, 2020;Long et al, 2021;Zhao et al, 2021): Where y is the number of sample labels; p t represents the probability of belonging to the plum category; α t is the coefficient of balancing the weight of positive and negative samples, 0 < α t < 1; γ is the modulation parameter for complex samples.…”

Section: Improvement Of the Loss Functionmentioning

confidence: 99%

“… Kuznetsova et al (2020) proposed a YOLOv3 apple detection algorithm with special pre-processing and post-processing. Li et al (2020) employed the MobileNet-YOLOv3 model to detect dragon fruit in the orchard. Wu et al (2021) proposed an improved YOLOv3 model based on clustering optimization.…”

Section: Introductionmentioning

confidence: 99%

Precision Detection of Dense Plums in Orchards Using the Improved YOLOv4 Model

et al. 2022

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The precision detection of dense small targets in orchards is critical for the visual perception of agricultural picking robots. At present, the visual detection algorithms for plums still have a poor recognition effect due to the characteristics of small plum shapes and dense growth. Thus, this paper proposed a lightweight model based on the improved You Only Look Once version 4 (YOLOv4) to detect dense plums in orchards. First, we employed a data augmentation method based on category balance to alleviate the imbalance in the number of plums of different maturity levels and insufficient data quantity. Second, we abandoned Center and Scale Prediction Darknet53 (CSPDarknet53) and chose a lighter MobilenetV3 on selecting backbone feature extraction networks. In the feature fusion stage, we used depthwise separable convolution (DSC) instead of standard convolution to achieve the purpose of reducing model parameters. To solve the insufficient feature extraction problem of dense targets, this model achieved fine-grained detection by introducing a 152 × 152 feature layer. The Focal loss and complete intersection over union (CIOU) loss were joined to balance the contribution of hard-to-classify and easy-to-classify samples to the total loss. Then, the improved model was trained through transfer learning at different stages. Finally, several groups of detection experiments were designed to evaluate the performance of the improved model. The results showed that the improved YOLOv4 model had the best mean average precision (mAP) performance than YOLOv4, YOLOv4-tiny, and MobileNet-Single Shot Multibox Detector (MobileNet-SSD). Compared with some results from the YOLOv4 model, the model size of the improved model is compressed by 77.85%, the parameters are only 17.92% of the original model parameters, and the detection speed is accelerated by 112%. In addition, the influence of the automatic data balance algorithm on the accuracy of the model and the detection effect of the improved model under different illumination angles, different intensity levels, and different types of occlusions were discussed in this paper. It is indicated that the improved detection model has strong robustness and high accuracy under the real natural environment, which can provide data reference for the subsequent orchard yield estimation and engineering applications of robot picking work.

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“…To realize the intelligent detection of fruits in the natural environment, researchers worldwide have successively explored and studied several solutions. Li et al proposed an improved YOLOv3 lightweight model combined with the Mobile Net method for ripe fruit detection and applied it to dragon fruit detection in the actual environment [6]. Bi et al used multiple segmentation methods to recognize citrus targets in the natural environment and improved multi-scale image detection and real-time performance of the citrus object detection model [7].…”

Section: Introductionmentioning

confidence: 99%

Green Citrus Detection and Counting in Orchards Based on YOLOv5-CS and AI Edge System

Lyu

Zhao

et al. 2022

Sensors

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Green citrus detection in citrus orchards provides reliable support for production management chains, such as fruit thinning, sunburn prevention and yield estimation. In this paper, we proposed a lightweight object detection YOLOv5-CS (Citrus Sort) model to realize object detection and the accurate counting of green citrus in the natural environment. First, we employ image rotation codes to improve the generalization ability of the model. Second, in the backbone, a convolutional layer is replaced by a convolutional block attention module, and a detection layer is embedded to improve the detection accuracy of the little citrus. Third, both the loss function CIoU (Complete Intersection over Union) and cosine annealing algorithm are used to get the better training effect of the model. Finally, our model is migrated and deployed to the AI (Artificial Intelligence) edge system. Furthermore, we apply the scene segmentation method using the “virtual region” to achieve accurate counting of the green citrus, thereby forming an embedded system of green citrus counting by edge computing. The results show that the mAP@.5 of the YOLOv5-CS model for green citrus was 98.23%, and the recall is 97.66%. The inference speed of YOLOv5-CS detecting a picture on the server is 0.017 s, and the inference speed on Nvidia Jetson Xavier NX is 0.037 s. The detection and counting frame rate of the AI edge system-side counting system is 28 FPS, which meets the counting requirements of green citrus.

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Pitaya detection in orchards using the MobileNet-YOLO model

Cited by 23 publications

References 12 publications

Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms

Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms

Precision Detection of Dense Plums in Orchards Using the Improved YOLOv4 Model

Green Citrus Detection and Counting in Orchards Based on YOLOv5-CS and AI Edge System

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