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

Lyu, Shilei; Li, Ruiyao; Zhao, Yawen; Zhen, Li; Fan, Renjie; Liu, Siying

doi:10.3390/s22020576

Cited by 38 publications

(18 citation statements)

References 23 publications

(24 reference statements)

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“…Before the training of the model, this study performed several random combinations of offline data augmentation methods such as flipping, clipping, rotation, scaling, translation, brightness, histogram averaging, salt and pepper noise, and Gaussian noise on the training set ( Taylor and Nitschke, 2018 ; Lyu et al, 2022 ). The five data augmentation methods were specifically (1) random horizontal flip of 25% of the training set + random vertical flip of 25% of the training set + random crop of 0–20% region of the image width/height; (2) histogram averaging + pepper noise 2%; (3) rotation 10°; (4) random modification of the brightness to 50–150% of the original + random Gaussian noise; (5) random scaling transformation 70–95% + random panning (−15–15%).…”

Section: Methodsmentioning

confidence: 99%

Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

et al. 2022

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The fast and precise detection of dense litchi fruits and the determination of their maturity is of great practical significance for yield estimation in litchi orchards and robot harvesting. Factors such as complex growth environment, dense distribution, and random occlusion by leaves, branches, and other litchi fruits easily cause the predicted output based on computer vision deviate from the actual value. This study proposed a fast and precise litchi fruit detection method and application software based on an improved You Only Look Once version 5 (YOLOv5) model, which can be used for the detection and yield estimation of litchi in orchards. First, a dataset of litchi with different maturity levels was established. Second, the YOLOv5s model was chosen as a base version of the improved model. ShuffleNet v2 was used as the improved backbone network, and then the backbone network was fine-tuned to simplify the model structure. In the feature fusion stage, the CBAM module was introduced to further refine litchi’s effective feature information. Considering the characteristics of the small size of dense litchi fruits, the 1,280 × 1,280 was used as the improved model input size while we optimized the network structure. To evaluate the performance of the proposed method, we performed ablation experiments and compared it with other models on the test set. The results showed that the improved model’s mean average precision (mAP) presented a 3.5% improvement and 62.77% compression in model size compared with the original model. The improved model size is 5.1 MB, and the frame per second (FPS) is 78.13 frames/s at a confidence of 0.5. The model performs well in precision and robustness in different scenarios. In addition, we developed an Android application for litchi counting and yield estimation based on the improved model. It is known from the experiment that the correlation coefficient R2 between the application test and the actual results was 0.9879. In summary, our improved method achieves high precision, lightweight, and fast detection performance at large scales. The method can provide technical means for portable yield estimation and visual recognition of litchi harvesting robots.

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Section: Methodsmentioning

confidence: 99%

Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

et al. 2022

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“…An improved YOLOv5 model was proposed to improve the detection of kiwi defects by adding a small target layer, introducing SE attention mechanism and CIoU loss function, and the mAP of the improved model was improved by nearly 9% compared with the original model ( Yao et al., 2021 ). An improved YOLOv5 model incorporating the involution bottleneck module and the SE module was proposed ( Chen et al., 2022 ), as well as a lightweight YOLOv5-CS model that improves the generalization capability of the model using image rotation coding to achieve accurate counting of citrus by deploying it into an AI edge system ( Lyu et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Precision detection of crop diseases based on improved YOLOv5 model

Zhao

Yang

et al. 2023

Front. Plant Sci.

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Accurate identification of crop diseases can effectively improve crop yield. Most current crop diseases present small targets, dense numbers, occlusions and similar appearance of different diseases, and the current target detection algorithms are not effective in identifying similar crop diseases. Therefore, in this paper, an improved model based on YOLOv5s was proposed to improve the detection of crop diseases. First, the CSP structure of the original model in the feature fusion stage was improved, and a lightweight structure was used in the improved CSP structure to reduce the model parameters, while the feature information of different layers was extracted in the form of multiple branches. A structure named CAM was proposed, which can extract global and local features of each network layer separately, and the CAM structure can better fuse semantic and scale inconsistent features to enhance the extraction of global information of the network. In order to increase the number of positive samples in the model training process, one more grid was added to the original model with three grids to predict the target, and the formula for the prediction frame centroid offset was modified to obtain the better prediction frame centroid offset when the target centroid falled on the special point of the grid. To solve the problem of the prediction frame being scaled incorrectly during model training, an improved DIoU loss function was used to replace the GIoU loss function used in the original YOLOv5s. Finally, the improved model was trained using transfer learning, the results showed that the improved model had the best mean average precision (mAP) performance compared to the Faster R-CNN, SSD, YOLOv3, YOLOv4, YOLOv4-tiny, and YOLOv5s models, and the mAP, F1 score, and recall of the improved model were 95.92%, 0.91, and 87.89%, respectively. Compared with YOLOv5s, they improved by 4.58%, 5%, and 4.78%, respectively. The detection speed of the improved model was 40.01 FPS, which can meet the requirement of real-time detection. The results showed that the improved model outperformed the original model in several aspects, had stronger robustness and higher accuracy, and can provide better detection for crop diseases.

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“…Very few attempts have been made to design robotic system with deep learning algorithms. [11][12][13][14][15] Use of Jetson Nano devices with Tensor RT makes light weight embedded devices which improves the detection speed 16 and the parallel computing NVIDIA deep learning accelerator (NVDLA) engines 17 provides additional platform for developing robotic systems with deep learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Kumar

Mohan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Progressive application of multidisciplinary research and development pushes the evolution of automation in many subsectors of agriculture to increase productivity, economic growth and environmental preservation with the help of robotics and artificial intelligence. Fruit harvesting robots have been developed mainly to provide support in the field for limited labour resources, to enable selective harvesting, to improve the efficiency and to preserve the quality of fruits. Even a small delay in harvesting can cause a maximum impact to the quality of the fruit. Selective fruit harvesting is an integration of different subcomponents. This paper provides a brief analysis of the techniques in selective fruit harvesting for the past 6 years starting from 2017 to 2022, associated principles, limitations and directions for future challenges. The first subcomponent is the vision system, it captures the information about the fruit in a tree canopy to perform efficient 2D and 3D localization. Hence getting accurate information from the vision system is more essential even in a complex agricultural environment. The detection of fruit from the vision system is normally done with two major methods such as traditional image processing and deep learning approaches. The result shows that the traditional methods provide high efficiency but the colour similarity, complex backgrounds and lightning conditions often makes failure in detection. Shortage of standard dataset and high-powered processing devices hinders the development of deep learning algorithms. Also, the usage of large data sets reduces the training speed even in pre-trained networks. For fruit grasping and detachment, the detection of plucking point is more essential to preserve the quality and for further storage. The elaborate description about the framework, limitations in current sensing, fruit and picking point detection algorithms provides guidelines to the researchers in building a fully automated robotic system to increase the processing speed and production rate.

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

Cited by 38 publications

References 23 publications

Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

Precision detection of crop diseases based on improved YOLOv5 model

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

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