Strawberry R-CNN: Recognition and counting model of strawberry based on improved faster R-CNN

Li, Jiajun; Zhu, Zhihong; Liu, Hongxin; Su, Yuanyuan; Deng, Limiao

doi:10.1016/j.ecoinf.2023.102210

Cited by 10 publications

(7 citation statements)

References 37 publications

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“…Based on the size of this parameter, different colors were used to draw regional growth maps, including strawberry growth parameters α. The calculation process is shown in Equation (14).…”

Section: Growth Information Mapmentioning

confidence: 99%

“…The calculation process is shown in Equation ( 14). (14) where num a , num b , and num c represented the number of mature strawberries, immature strawberries, and strawberry flowers, respectively.…”

Section: Growth Information Mapmentioning

confidence: 99%

“…The DSE (detail-semantics enhancement) module, which uses dilated convolution, effectively addresses the maturity detection issue for small target strawberries [13]. VGG-16 was replaced by an improved multi-cascade network structure in the Faster R-CNN module, achieving precise recognition and counting of strawberries [14]. Wise iou is a loss function that can accelerate convergence speed.…”

Section: Introductionmentioning

confidence: 99%

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Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Wang,

Han,

et al. 2024

Agriculture

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Reasonably formulating the strawberry harvesting sequence can improve the quality of harvested strawberries and reduce strawberry decay. Growth information based on drone image processing can assist the strawberry harvesting, however, it is still a challenge to develop a reliable method for object identification in drone images. This study proposed a deep learning method, including an improved YOLOv8 model and a new image-processing framework, which could accurately and comprehensively identify mature strawberries, immature strawberries, and strawberry flowers in drone images. The improved YOLOv8 model used the shuffle attention block and the VoV–GSCSP block to enhance identification accuracy and detection speed. The environmental stability-based region segmentation was used to extract the strawberry plant area (including fruits, stems, and leaves). Edge extraction and peak detection were used to estimate the number of strawberry plants. Based on the number of strawberry plants and the distribution of mature strawberries, we draw a growth chart of strawberries (reflecting the urgency of picking in different regions). The experiment showed that the improved YOLOv8 model demonstrated an average accuracy of 82.50% in identifying immature strawberries, 87.40% for mature ones, and 82.90% for strawberry flowers in drone images. The model exhibited an average detection speed of 6.2 ms and a model size of 20.1 MB. The proposed new image-processing technique estimated the number of strawberry plants in a total of 100 images. The bias of the error for images captured at a height of 2 m is 1.1200, and the rmse is 1.3565; The bias of the error for the images captured at a height of 3 m is 2.8400, and the rmse is 3.0199. The assessment of picking priorities for various regions of the strawberry field in this study yielded an average accuracy of 80.53%, based on those provided by 10 experts. By capturing images throughout the entire growth cycle, we can calculate the harvest index for different regions. This means farmers can not only obtain overall ripeness information of strawberries in different regions but also adjust agricultural strategies based on the harvest index to improve both the quantity and quality of fruit set on strawberry plants, as well as plan the harvesting sequence for high-quality strawberry yields.

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“…Based on the size of this parameter, different colors were used to draw regional growth maps, including strawberry growth parameters α. The calculation process is shown in Equation (14).…”

Section: Growth Information Mapmentioning

confidence: 99%

Section: Growth Information Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Wang,

Han,

et al. 2024

Agriculture

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show abstract

“…The learning rate experiment is designed to analyze and compare the effects of different learning rates on the model [29], with a batch size of 48 and 300 training rounds, in order to determine the optimal learning rate. Setting an excessively high learning rate will impede network convergence, while setting it too low will result in sluggish convergence and a prolonged search for the optimal value.…”

Section: Impact Of Learning Rate On the Modelmentioning

confidence: 99%

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Xu,

Fu,

Gao

et al. 2024

Agriculture

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The origin of seeds is a crucial environmental factor that significantly impacts crop production. Accurate identification of seed origin holds immense importance for ensuring traceability in the seed industry. Currently, traditional methods used for identifying the origin of maize seeds involve mineral element analysis and isotope fingerprinting, which are laborious, destructive, time-consuming, and suffer from various limitations. In this experiment, near-infrared spectroscopy was employed to collect 1360 maize seeds belonging to 12 different varieties from 8 distinct origins. Spectral information within the range of 11,550–3950 cm−1 was analyzed while eliminating multiple interferences through first-order derivative combined with standard normal transform (SNV). The processed one-dimensional spectral data were then transformed into three-dimensional spectral maps using Gram’s Angle Field (GAF) to be used as input values along with the VGG-19 network model. Additionally, a convolution layer with a step size of 1 × 1 and the padding value set at 1 was added, while pooling layers had a step size of 2 × 2. A batch size of 48 and learning rate set at 10−8 were utilized while incorporating the Dropout mechanism to prevent model overfitting. This resulted in the construction of the GAF-VGG network model which successfully decoded the output into accurate place-of-origin labels for maize seed detection. The findings suggest that the GAF-VGG network model exhibits significantly superior performance compared to both the original data and the PCA-based origin identification model in terms of accuracy, recall, specificity, and precision (96.81%, 97.23%, 95.35%, and 95.12%, respectively). The GAF-VGGNet model effectively captures the NIR features of different origins of maize seeds without requiring feature wavelength extraction, thereby reducing training time and enhancing accuracy in identifying maize seed origin. Moreover, it simplifies near-infrared (NIR) spectral modeling complexity and presents a novel approach to maize seed origin identification and traceability analysis.

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“…It can detect objects in real-time and is suitable for use on mobile devices. With the ability to detect up to 325 objects per image, YoloV8 achieves a mean average precision (mAP) of 42.4% on the COCO dataset [11]. This model is a powerful tool for object detection, particularly in real-time applications such as self-driving cars and robotics.…”

Section: Introductionmentioning

confidence: 99%

Waste Detection and Water Quality Assessment in Aquatic Environments: A Comprehensive Study Using YoloV8 and XGBoost

Parasar,

Vadalia,

Chavan

et al. 2024

Preprint

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This research paper presents a structured approach to address the critical concerns associated with water quality assessment and underwater waste detection, employing advanced machine learning techniques. It commences with an exposition on the significance of water pollution's impact on aquatic ecosystems. Subsequently, the methodology employed in this study encompasses the utilization of the YOLOv8 model for the identification of underwater waste, a rule-based classifier for the evaluation of water quality, and the application of the XGBoost algorithm for predicting water potability. The ensuing sections delve into the practical implementation of these components, offering in-depth insights into their technical intricacies and seamless integration. A thorough evaluation follows, substantiating the system's effectiveness and reliability in three key dimensions: underwater waste detection, water quality assessment, and water potability prediction. As indicated by the lower map50-95 score, the Yolov8 model showed impressive precision and recall in recognising positive cases; however, improvements are required for complex object detection scenarios. Analysing the confusion matrix revealed particular categories that needed to be improved. On the other hand, the XGBoost classifier produced encouraging outcomes, demonstrating excellent accuracy, f1 score, precision, and recall in a variety of categories, underscoring its efficacy in precise sample class prediction. The research paper concludes by underscoring the transformative potential of this multifaceted approach in bolstering environmental conservation and safeguarding aquatic ecosystems against the pernicious effects of water pollution.

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Strawberry R-CNN: Recognition and counting model of strawberry based on improved faster R-CNN

Cited by 10 publications

References 37 publications

Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Waste Detection and Water Quality Assessment in Aquatic Environments: A Comprehensive Study Using YoloV8 and XGBoost

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