Pollen Grains Classification with a Deep Learning System GPU-Trained

Cisneros, Susana Ortega; Varela, Juan M.; Rivera-Acosta, Miguel; Domínguez, Jorge Domínguez; Villalobos, Pablo Moreno

doi:10.1109/tla.2022.9662170

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…With the rapid development of artificial intelligence in recent years, deep learning technology has also made great progress [12][13][14][15]. Deep learning, in which machines mimic human activities such as seeing, hearing, and thinking to solve complex pattern recognition challenges, has been successfully applied to rapid crop and fruit detection [16].WILSON et al [17] determined the ripeness of The Cape gooseberry fruit by a combination of machine learning techniques and color spaces (RGB, HSV, and L*a*b*).…”

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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“…Advancements in imaging technology and computational methodologies have opened new avenues for pollen analysis automation. While various machine learning techniques have shown promise in honey analysis, challenges remain in achieving high accuracy and processing speed. − YOLOv7, a convolutional neural network, has revolutionized object detection, demonstrating effectiveness in agriculture and food identification. YOLOv7 is the advanced state-of-the-art detector in the YOLO (you only look once) series .…”

Section: Introductionmentioning

confidence: 99%

Detection and Identification of Honey Pollens by YOLOv7: A Novel Framework toward Honey Authenticity

Jubayer,

Ruhad,

Kayshar

et al. 2024

ACS Agric. Sci. Technol.

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Honey, a valuable and globally consumed food product, has significant market potential linked to its origin. However, authenticating honey is challenging due to sophisticated adulteration techniques. This current research introduces an innovative approach employing YOLOv7, a cutting-edge object detection model, to detect and classify honey pollens, thereby bolstering the authentication of honey. Our methodology involved creating a data set comprising three well-known honey varieties (Sundarban, Litchi, and Mustard), supplemented by three sets of unidentified honey pollen images sourced from Kaggle. Subsequently, we assembled a data set consisting of 3000 images representing the pollen types extracted from the known honey samples. To tackle the challenge of limited sample sizes, we employed data augmentation techniques. The efficacy of our approach was evaluated using established statistical measures including detection accuracy, precision, recall, mAP value, and F1 score, yielding impressive values of 98.3, 99.3, 100, 99.2%, and 0.985, respectively. The YOLOv7 model's reliability was validated using Kaggle's unknown honey pollen data sets, which showed that it correctly detected and identified these new pollens based on previous training. Through rigorous experimentation and validation, our study underscores the potential of the YOLOv7 framework in revolutionizing quality control practices within the honey industry, ensuring consumers access to genuine and top-tier honey products through pollen image analysis.

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Human Gender Classification of Males and Females in a Crowd Using Deep Learning Techniques

Kachhawa,

Mahajan

2024

Data-Intensive Research

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Pollen Grains Classification with a Deep Learning System GPU-Trained

Cited by 8 publications

References 19 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

Detection and Identification of Honey Pollens by YOLOv7: A Novel Framework toward Honey Authenticity

Human Gender Classification of Males and Females in a Crowd Using Deep Learning Techniques

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