Tomato Fruit Detection Using Modified Yolov5m Model with Convolutional Neural Networks

Tsai, Fa-Ta; Nguyen, Van-Tung; Duong, The-Phong; Phan, Quoc-Hung; Lien, Chi-Hsiang

doi:10.3390/plants12173067

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…Firstly, the GhostConv module replaced the Conv module in the original CBS module, in order to improve detection speed. The conventional feature extraction method uses multiple convolutions to check all channels in the input feature map [32][33][34]. Stacking convolutional layers in deep networks requires many parameters and significant computation resources, producing many rich and even redundant feature graphs.…”

Section: Improved Yolov7 Networkmentioning

confidence: 99%

Recognition and Positioning of Strawberries Based on Improved YOLOv7 and RGB-D Sensing

Li,

Wang,

Guo

et al. 2024

Agriculture

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To improve the speed and accuracy of the methods used for the recognition and positioning of strawberry plants, this paper is concerned with the detection of elevated-substrate strawberries and their picking points, using a strawberry picking robot, based on the You Only Look Once version 7 (YOLOv7) object detection algorithm and Red Green Blue-Depth (RGB-D) sensing. Modifications to the YOLOv7 model include the integration of more efficient modules, incorporation of attention mechanisms, elimination of superfluous feature layers, and the addition of layers dedicated to the detection of smaller targets. These modifications have culminated in a lightweight and improved YOLOv7 network model. The number of parameters is only 40.3% of that of the original model. The calculation amount is reduced by 41.8% and the model size by 59.2%. The recognition speed and accuracy are also both improved. The frame rate of model recognition is increased by 19.3%, the accuracy of model recognition reaches 98.8%, and mAP@0.95 reaches 96.8%. In addition, we have developed a method for locating strawberry picking points based on strawberry geometry. The test results demonstrated that the average positioning success rate and average positioning time were 90.8% and 76 ms, respectively. The picking robot in the laboratory utilized the recognition and positioning method proposed in this paper. The error of hand–eye calibration is less than 5.5 mm on the X-axis, less than 1.6 mm on the Y-axis, and less than 2.7 mm on the Z-axis, which meets the requirements of picking accuracy. The success rate of the picking experiment was about 90.8%, and the average execution time for picking each strawberry was 7.5 s. In summary, the recognition and positioning method proposed in this paper provides a more effective method for automatically picking elevated-substrate strawberries.

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Section: Improved Yolov7 Networkmentioning

confidence: 99%

Recognition and Positioning of Strawberries Based on Improved YOLOv7 and RGB-D Sensing

Li,

Wang,

Guo

et al. 2024

Agriculture

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“…They used validation metrics like accuracy, precision, sensitivity, specificity, and F1-score to obtain the best results with the GoogleNet architecture, reporting the highest value of 98.73% for specificity. Tsai et al [29] implemented the model YoloV5m integrated with BoTNet, ShuffleNet, and GhostNet to detect tomato fruits, reporting accuracies of 94%, 95%, and 96% for these models, respectively.…”

Section: Introductionmentioning

confidence: 99%

Pattern Classification of an Onion Crop (Allium Cepa) Field Using Convolutional Neural Network Models

López-Martínez,

Díaz-Flórez,

Villagrana-Barraza

et al. 2024

Agronomy

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Agriculture is an area that currently benefits from the use of new technologies and techniques, such as artificial intelligence, to improve production in crop fields. Zacatecas is one of the states producing the most onions in the northeast region of Mexico. Identifying and determining vegetation, soil, and humidity zones could help solve problems such as irrigation demands or excesses, identify spaces with different levels of soil homogeneity, and estimate the yield or health of the crop. This study examines the application of artificial intelligence through the use of deep learning, specifically convolutional neural networks, to identify the patterns that can be found in a crop field, in this case, vegetation, soil, and humidity zones. To extract the mentioned patterns, the K-nearest neighbor algorithm was used to pre-process images taken using unmanned aerial vehicles and form a dataset composed of 3672 images of vegetation, soil, and humidity (1224 for each class). A total of six convolutional neural network models were used to identify and classify the patterns, namely Alexnet, DenseNet, VGG16, SqueezeNet, MobileNetV2, and Res-Net18. Each model was evaluated with the following validation metrics: accuracy, F1-score, precision, and recall. The results showed a variation in performance between 90% and almost 100%. Alexnet obtained the highest metrics with an accuracy of 99.92%, while MobileNetV2 had the lowest accuracy of 90.85%. Other models, such as DenseNet, VGG16, SqueezeNet, and ResNet18, showed an accuracy of between 92.02% and 98.78%. Furthermore, our study highlights the importance of adopting artificial intelligence in agriculture, particularly in the management of onion fields in Zacatecas, Mexico. The findings can help farmers and agronomists make more informed and efficient decisions, which can lead to greater production and sustainability in local agriculture.

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“…One prevalent approach involves constructing a detection model using machine learning algorithms. 15–17 Guo et al proposed a model for wolfberry classification, achieving an impressive accuracy of 99%, thereby significantly improving the precision and efficiency of wolfberry classification. 18 Hyperparameter optimization is a common method for improving model performance.…”

Section: Introductionmentioning

confidence: 99%

Air pressure prediction model based on the fusion of laser-induced plasma images and spectra

Ke,

Luo,

et al. 2024

J. Anal. At. Spectrom.

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Tomato Fruit Detection Using Modified Yolov5m Model with Convolutional Neural Networks

Cited by 10 publications

References 44 publications

Recognition and Positioning of Strawberries Based on Improved YOLOv7 and RGB-D Sensing

Recognition and Positioning of Strawberries Based on Improved YOLOv7 and RGB-D Sensing

Pattern Classification of an Onion Crop (Allium Cepa) Field Using Convolutional Neural Network Models

Air pressure prediction model based on the fusion of laser-induced plasma images and spectra

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