Real-time strawberry detection using deep neural networks on embedded system (rtsd-net): An edge AI application

Zhang, Yanchao; Yu, Jiya; Yang, Chen; Yang, Wen; Zhang, Wenbo; He, Yong

doi:10.1016/j.compag.2021.106586

Cited by 86 publications

(39 citation statements)

References 10 publications

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“…Chen et al [22] added the identification of strawberry flowers to the category of detected ripe and immature strawberries and estimated their yield based on the FAST-RCNN algorithm whose average accuracy of detection was 72%. Li et al [23]used a combination of depth features and classifiers for strawberry appearance recognition, which was mainly applied to mechanized production lines, and the accuracy of the method was as high as 96.55%, but its single background was not suitable for detection under natural growth environment.ZHANG [24] designed a lightweight real-time strawberry recognition device based on the YOLOv4-tiny algorithm, which is mainly applied to ripe strawberry harvest detection.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Monitoring Method of Strawberry Fruit Growth State Based on YOLO Improved Model

Wang

et al. 2022

IEEE Access

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A key challenge for automated orchard management robots is the rapid and accurate identification of crop growth and maturity conditions for subsequent operations, such as automatic pollination, fertilization, and picking. In particular, strawberries have a short ripening period and the fruits are heavily overlapped and shaded by each other, which is time-consuming and inefficient under traditional detection methods. Therefore, we designed and developed a strawberry growth detection algorithm, SDNet (Strawberry Detect Net). The algorithm is based on the YOLOX model and replaces the original CSP block in the backbone network with a self-designed feature extraction module C3HB block to improve the spatial interaction capability and monitoring accuracy of the detection algorithm; Then, the normalized attention module (NAM) is embedded in the neck to improve the detection accuracy and attention weight of small target fruits; and we use the latest SIOU objective loss function to improve the prediction accuracy of the detection model, which finally achieves the monitoring of strawberry fruits under five growth states. The experimental results show that the mAP, precision, and recall of SDNet are 94.26%, 93.15%, and 90.72%, respectively, and the monitoring speed is 30.5 ms. It is 4.08%, 3.64 and 2.04% higher than the precision, accuracy, and recall of YOLOX, respectively, and there is no significant change in the model size. The research results can effectively solve the problem of low accuracy of strawberry fruit growth state monitoring under complex environments, and provide important technical reference for realizing unmanned farm and precision agriculture.INDEX TERMS Fruit detection,Object detection,Real-time counting,Digital agriculture.

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Section: Introductionmentioning

confidence: 99%

Real-Time Monitoring Method of Strawberry Fruit Growth State Based on YOLO Improved Model

Wang

et al. 2022

IEEE Access

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“…Therefore, it is necessary to optimize and improve the existing network model to meet the detection needs of this research. Currently, object detection technology has been used in strawberries [30][31][32], grapes [33], apple fruits [34,35], flowers [36], maize [25], and rice [37,38], and has achieved relatively good application results. There is scientific evidence about the accuracy of the proposed architecture described in the studies by Li et al [39], in which YOLO-JD has achieved the best detection accuracy, with an average mAP of 96.63%.…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

et al. 2022

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Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method based on the optimized lightweight YOLOv5 model is proposed. We propose an IASM mechanism to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer. To verify the effect of the optimized model, we conducted a performance comparison test and ablation test between the optimized model and other mainstream models. The results show that the operation time and accuracy of the optimized model are 11.8% and 3.98% higher than the original model, respectively, while F1 score reaches 92.65%, which highlight statistical metrics better than the current mainstream models. Moreover, the classification accuracy rate on the self-made dataset reaches 92.57%, indicating the effectiveness of the plant disease classification model proposed in this paper, and the transfer learning ability of the model can be used to expand the application scope in the future.

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“…(2017), has been utilised in flower detection studies, as flowers are most visible from above the canopy. A consideration when using CNNs for high‐throughput phenotyping is how to balance the trade‐off between accuracy (for example, using Faster R‐CNN type networks (Chen et al., 2019; Lin & Chen, 2018; Zhou et al., 2020) and efficiency (such as the YOLO family of architectures (Fan et al., 2022; Kim et al., 2020; Zhang et al., 2022)) if real‐time performance is of relevance in the system. The automation of fruit and flower counts is beneficial to the selection process as the productivity of genotypes can be assessed, allowing breeders to immediately disregard those that do not meet the required level and thus increasing the selection process efficiency.…”

Section: Automation Of Morphological Traits Currently Used In Breedingmentioning

confidence: 99%

“…As fruit and flower counts are also vital for yield forecasting applications, the precise detection and counting of these components have been a research focus for high‐throughput methods, with around half of the literature pertaining to automation of phenotypic traits centring around this. In image‐based phenotyping, CNNs have been used to address fruit (Chen et al., 2019; Fan et al., 2022; Ilyas et al., 2021; Kerfs et al., 2017; Kim et al., 2020; Kirk et al., 2020; Lamb & Chuah, 2018; Yu et al., 2019; Zhang et al., 2022; Zhou et al., 2020) and flower (Heylen et al., 2021; Lin & Chen, 2018) detection in real‐world agricultural conditions, as they offer greater robustness to the varying environmental conditions experienced than traditional machine learning methods that use manually defined features. However, a challenge for detection in real‐world environments is occlusion, which can be minimised by selecting an appropriate viewpoint from which to collect data so as to maximise the prominence of the organ of interest in the image.…”

Section: Automation Of Morphological Traits Currently Used In Breedingmentioning

confidence: 99%

High‐throughput phenotyping for breeding targets—Current status and future directions of strawberry trait automation

James

Whitehouse

Cielniak

2022

Plants People Planet

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Strawberry breeders are faced with increasing demands by propagators, growers, retailers and consumers for particular agronomic traits. This and the volume of plants requiring assessment during selection constrain breeders to rapid and qualitative rating methods. High-throughput systems for assessing these traits automatically could indicate which families, or individual genotypes, should be singled out for further, more thorough evaluation, thus significantly increasing the selection intensity and accuracy. This review assesses the current status of and future potential for automated phenotyping in strawberry crops, highlighting key advances and the gaps which need to be addressed to facilitate the development of such technology.

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Real-time strawberry detection using deep neural networks on embedded system (rtsd-net): An edge AI application

Cited by 86 publications

References 10 publications

Real-Time Monitoring Method of Strawberry Fruit Growth State Based on YOLO Improved Model

Real-Time Monitoring Method of Strawberry Fruit Growth State Based on YOLO Improved Model

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

High‐throughput phenotyping for breeding targets—Current status and future directions of strawberry trait automation

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