Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting

Divyanth, L. G.; Soni, Peeyush; Pareek, C.M.; Machavaram, Rajendra; Nadimi, Mohammad; Paliwal, Jitendra

doi:10.3390/foods11233903

Cited by 17 publications

(4 citation statements)

References 67 publications

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“…In addition, a certain degree of occlusion in other fruits also affects label generation accuracy. In a recent study, we noticed some work [ 49 , 50 ] to improve detection performance by setting up attention mechanisms in the model for the fruit occlusion problem. We plan to subsequently analyze this idea and to conduct further research to improve the effectiveness of fruit auto labeling.…”

Section: Discussionmentioning

confidence: 99%

DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling

Zhang,

Zheng,

Wang

et al. 2024

Plant Phenomics

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Recently, deep learning-based fruit detection applications have been widely used in the modern fruit industry; however, the training data labeling process remains a time-consuming and labor-intensive process. Auto labeling can provide a convenient and efficient data source for constructing smart orchards based on deep-learning technology. In our previous study, based on a labeled source domain fruit dataset, we used a generative adversarial network and a fruit detection model to achieve auto labeling of unlabeled target domain fruit images. However, since the current method uses one species source domain fruit to label multiple species target domain fruits, there is a problem of the domain gap in both the foreground and the background between the training data (retaining the source domain fruit label information) and the application data (target domain fruit images) of the fruit detection model. Therefore, we propose a domain-adaptive anchor-free fruit detection model, DomAda-FruitDet, and apply it to the previously proposed fruit labeling method to further improve the accuracy. It consists of 2 design aspects: (a) With a foreground domain-adaptive structure based on double prediction layers, an anchor-free method with multiscale detection capability is constructed to generate adaptive bounding boxes that overcome the foreground domain gap; (b) with a background domain-adaptive strategy based on sample allocation, we enhance the ability of the model to extract foreground object features to overcome the background domain gap. As a result, the proposed method can label actual apple, tomato, pitaya, and mango datasets, with an average precision of 90.9%, 90.8%, 88.3%, and 94.0%, respectively. In conclusion, the proposed DomAda-FruitDet effectively addressed the problem of the domain gap and improved effective auto labeling for fruit detection tasks.

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Section: Discussionmentioning

confidence: 99%

DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling

Zhang,

Zheng,

Wang

et al. 2024

Plant Phenomics

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“…As a result of the study, the Yolov5 algorithm was found to be more successful and the Yolov5 algorithm was recommended for walnut detection. Divyanth et al [10] obtained images under different light and at different angles for coconut detection. By performing augmentation on the obtained data, 900 image data were obtained.…”

Section: Related Workmentioning

confidence: 99%

An improved pistachio detection approach using YOLO-v8 Deep Learning Models

Gökalp,

Mübarek Mazhar

2024

BIO Web Conf.

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Pistachios are an agricultural product widely used in the food industry. It is very important that pistachios are presented to the consumer in good quality on time. At the same time, whether the shells of pistachios are open or closed is an important criterion from a commercial industrial point of view. Pistachios with their shells open have a high unsaturated fat content, a high maturity level and an expensive market value. In this study, the open or closed status of pistachios was determined by using Artificial Intelligence-based deep learning models. For pistachio detection, 423 image data belonging to the Pesteh dataset were classified using models of the Yolov8 algorithm, which detects objects using convolutional neural networks. The data set is divided into 80% training, 10% validation and 10% testing. The performances of the models were evaluated with precision, recall, F1 and mAP score metrics. The highest test mAP value of the Yolov8 algorithm, which was run with image data consisting of pistachios, was obtained with the Yolov8-m model with 94.8%. The Yolov8-m model achieved a very successful result with 49.6 MB weight size, 11.0 ms inference time value and 0.33 hours training time value. In addition, the model's fast classification performance and small file size facilitate its applicability in the industrial field. The results show that the classification and detection of open and closed shell pistachios has been successfully carried out with Yolo models.

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“…Machine vision-based technique is another tool that has demonstrated the potential to replace several manual (visual) methods of grain grade/quality inspection (Jayas & Singh, 2012;Vithu & Moses, 2016). Color imaging systems are machine vision tools that can evaluate sample characteristics according to their visual appearance on the exterior surface (Divyanth et al, 2022a(Divyanth et al, , 2022bHosainpour et al, 2022;Olakanmi et al, 2023;Kheiralipour et al, 2022;Sabzi et al, 2022). Some of their applications include the identification of dockage and foreign material (Paliwal et al, 2003), insect presence (Ridgway et al, 2002), insect infestation (Ebrahimi et al, 2014), microbial infection (Qiu et al, 2019), seed germination , broken kernels (Luo et al, 1999), as well as detection of grain grade/class/type according to kernel size, shape, color, or texture (Majumdar & Jayas, 2000a, 2000b, 2000c, 2000dSabanci et al, 2017).…”

Section: Modernizing Grain Quality Measurement: An Investigation Of T...mentioning

confidence: 99%

Enhancing traceability of wheat quality through the supply chain

Nadimi

Hawley²,

Liu

et al. 2023

Comp Rev Food Sci Food Safe

Self Cite

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With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.

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Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting

Cited by 17 publications

References 67 publications

DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling

DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling

An improved pistachio detection approach using YOLO-v8 Deep Learning Models

Enhancing traceability of wheat quality through the supply chain

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