TomatoDet: Anchor-free detector for tomato detection

Liu, Guoxu; Hou, Zengtian; Liu, Hongtao; Li, Jun; Zhao, Wei; Li, Kun

doi:10.3389/fpls.2022.942875

Cited by 9 publications

(7 citation statements)

References 42 publications

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“…Fruit detection from digital videos is based on computer vision and deep learning methods at present [39,41], which requires the use of computers and digital cameras instead of manual operations [5,7,10,11,17,28]. There are a lot of methods for fruit object detection [2,4,15,18,33]. The fruit detection essentially needs both classification and localization by providing the class labels and bounding box coordinates of the targets [8,14,16,26,27].…”

Section: Fruit Detectio Nmentioning

confidence: 99%

Fruit ripeness identification using YOLOv8 model

Xiao,

Nguyen,

Yan

2023

Multimed Tools Appl

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Deep learning-based visual object detection is a fundamental aspect of computer vision. These models not only locate and classify multiple objects within an image, but they also identify bounding boxes. The focus of this paper's research work is to classify fruits as ripe or overripe using digital images. Our proposed model extracts visual features from fruit images and analyzes fruit peel characteristics to predict the fruit's class. We utilize our own datasets to train two "anchor-free" models: YOLOv8 and CenterNet, aiming to produce accurate predictions. The CenterNet network primarily incorporates ResNet-50 and employs the deconvolution module DeConv for feature map upsampling. The final three branches of convolutional neural networks are applied to predict the heatmap. The YOLOv8 model leverages CSP and C2f modules for lightweight processing. After analyzing and comparing the two models, we found that the C2f module of the YOLOv8 model significantly enhances classification results, achieving an impressive accuracy rate of 99.5%.

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Section: Fruit Detectio Nmentioning

confidence: 99%

Fruit ripeness identification using YOLOv8 model

Xiao,

Nguyen,

Yan

2023

Multimed Tools Appl

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“…It is not limited to the influence of the object scale feature in the training data during prediction to avoid the shortcoming of the lack of flexibility in the generation of bounding boxes. Several scholars [ 31 – 34 ] have already applied the anchor-free detector for fruit detection. Liu et al.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al. [ 31 ] proposed TomatoDet for tomato detection, which avoids the complex hyperparameter design and low detection efficiency caused by exhaustive anchor boxes and classification operations in anchor-based detectors. However, the circular bounding boxes generated by this model are only suitable for tomatoes or other round fruits with aspect ratios close to 1:1, not for other species with substantial differences in aspect.…”

Section: Introductionmentioning

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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“…Early studies mainly reported the use of the following frameworks: early single-shot models, such as YOLO v3 [18,19] and SSD [3,20]; and double-shot models, such as faster RCNN [2,21] and Mask RCNN [1,22]. In addition to using state-of-the-art deep learning architectures in 2022 and beyond, research on improving parts of the network and optimizing it for tomato detection has become a central concern for researchers; YOLO v4 [20,23,24], YOLO v5 [25,26], YOLO X [27], YOLO v8 [28], and CornerNet [29].…”

Section: Introductionmentioning

confidence: 99%

Interoperability Analysis of Tomato Fruit Detection Models for Images Taken at Different Facilities, Cultivation Methods, and Times of the Day

Naito,

Shimomoto,

Fukatsu

et al. 2024

AgriEngineering

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This study investigated the interoperability of a tomato fruit detection model trained using nighttime images from two greenhouses. The goal was to evaluate the performance of the models in different environmets, including different facilities, cultivation methods, and imaging times. An innovative imaging approach is introduced to eliminate the background, highlight the target plants, and test the adaptability of the model under diverse conditions. The results demonstrate that the tomato fruit detection accuracy improves when the domain of the training dataset contains the test environment. The quantitative results showed high interoperability, achieving an average accuracy (AP50) of 0.973 in the same greenhouse and a stable performance of 0.962 in another greenhouse. The imaging approach controlled the lighting conditions, effectively eliminating the domain-shift problem. However, training on a dataset with low diversity or inferring plant appearance images but not on the training dataset decreased the average accuracy to approximately 0.80, revealing the need for new approaches to overcome fruit occlusion. Importantly, these findings have practical implications for the application of automated tomato fruit set monitoring systems in greenhouses to enhance agricultural efficiency and productivity.

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TomatoDet: Anchor-free detector for tomato detection

Cited by 9 publications

References 42 publications

Fruit ripeness identification using YOLOv8 model

Fruit ripeness identification using YOLOv8 model

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

Interoperability Analysis of Tomato Fruit Detection Models for Images Taken at Different Facilities, Cultivation Methods, and Times of the Day

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