Towards Precise Recognition of Pollen Bearing Bees by Convolutional Neural Networks

Monteiro, Fernando C.; Pinto, Cristina M.; Rufino, José

doi:10.1007/978-3-030-93420-0_21

Cited by 3 publications

(7 citation statements)

References 14 publications

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“…It is worth mentioning that the compared works used slightly different approaches to solve the pollen grain detection task. The first four works [24,27,29,30] detected pollen grains in cropped images where the bee was already centred. They used a relatively lower resolution in comparison to the next three works and did not investigate the time required to process one frame, while the works [23,25,26] solved the real-time multiple bee detection and localization problem upon entrance to the beehive along with pollen classification.…”

Section: Methodsmentioning

confidence: 99%

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FPGA Implementation of a Convolutional Neural Network and Its Application for Pollen Detection upon Entrance to the Beehive

2022

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The condition of a bee colony can be predicted by monitoring bees upon hive entrance. The presence of pollen grains gives beekeepers significant information about the well-being of the bee colony in a non-invasive way. This paper presents a field-programmable-gate-array (FPGA)-based pollen detector from images obtained at the hive entrance. The image dataset was acquired at native entrance ramps from six different hives. To evaluate and demonstrate the performance of the system, various densities of convolutional neural networks (CNNs) were trained and tested to find those suitable for pollen grain detection at the chosen image resolution. We propose a new CNN accelerator architecture that places a pre-trained CNN on an SoC FPGA. The CNN accelerator was implemented on a cost-optimized Z-7020 FPGA with 16-bit fixed-point operations. The kernel binarization and merging with the batch normalization layer were applied to reduce the number of DSPs in the multi-channel convolutional core. The estimated average performance was 32 GOPS for a single convolutional core. We found that the CNN with four convolutional and two dense layers gave a 92% classification accuracy, and it matched those declared for state-of-the-art methods. It took 8.8 ms to classify a 512 × 128 px frame and 2.4 ms for a 256 × 64 px frame. The frame rate of the proposed method outperformed the speed of known pollen detectors. The developed pollen detector is cost effective and can be used as a real-time image classification module for hive status monitoring.

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Section: Methodsmentioning

confidence: 99%

“…The authors achieved a maximal classification accuracy of 96% on the proposed dataset (710 pollen and non-pollen bee images in total) of cropped single bee-centred images. Monteiro et al [29] used the same dataset to train several known CNN models. The classification accuracy varied in the range 88-99%.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of a Convolutional Neural Network and Its Application for Pollen Detection upon Entrance to the Beehive

2022

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“…The obtained results from this study showed that the proposed model gives a classification accuracy of 94%. In another research, nine different pre-trained CNN models, including VGG16, VGG19, Resnet50, ResNet101, Inception V2, Inception V3, Xception, DenseNet201, and DarkNet53 have been explored [5]. The authors also considered the influence of color by applying some image preprocessing techniques to the input dataset.…”

Section: Related Workmentioning

confidence: 99%

“…In the context of recognizing pollen-bearing honeybee images, many CNN-based models have been designed. For example, several well-known CNNbased models like VGG16, VGG19, Resnet50, and DarkNet53 have been applied in [5] towards precise recognition of pollenbearing honeybees. Rodriguez et al [6] tested with several types of CNN architectures and showed that shallow-CNN architecture gives higher recognition accuracy than machine learning methods such as SVM (Support Vector Machine), Naive Bayes, or K-nearest neighbors.…”

Section: Introductionmentioning

confidence: 99%

A Novel Convolutional Neural Network Architecture for Pollen-Bearing Honeybee Recognition

Le,

Phan

et al. 2023

IJACSA

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Monitoring the pollen foraging behavior of honeybees is an important task that is beneficial to beekeepers, allowing them to understand the health status of their honeybee colonies. To perform this task, monitoring systems should have the ability to automatically recognize images of pollen-bearing honeybees extracted from videos recorded at the beehive entrance. In this paper, a novel convolutional neural network architecture is proposed for recognizing pollen-bearing and non-pollen-bearing honeybees from their images. The performance of the proposed model is illustrated based on a real dataset and the obtained results show that it performs better than some other state-of-theart deep learning architectures like VGG16, VGG19, or Resnet50 in terms of both accuracy and execution time. Thus, the proposed model can be considered an effective algorithm for designing automatic honeybee colony monitoring systems.

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“…Lebah berperan dalam penyerbukan tanaman dan mengahasilkan madu yang sangat bermanfaat bagi manusia [1]. Selain itu, peternakan lebah untuk menghasilkan madu saat ini cukup menjajikan karena berdasarkan penelitian madu memiliki berbagai macam kandungan yang dapat mendukup pemeliharan kesehatan manusia [2]- [7].…”

Section: Pendahuluanunclassified

Perbandingan Algoritma Xception dan VGG16 Untuk Pengenalan Lebah Pollen-Bearing

Noprisson¹,

Nurhaida

Purba

2022

JSAI

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Dengan adanya pengamatan yang terjadwal akan membantu pemelihara lebah dalam mengetahui penyakit lebah, kesehatan sarang lebah dan racun yang mungkin terbawa oleh lebah. Jika ini dapat dilakukan dengan bantuan komputer, maka ini akan mengurangi waktu dan biaya pemeliharaan lebah. Selain itu, produksi madu dan sarang akan akan meningkat baik dari sisi kualitas maupun kuantitas. Penelitian ini bertujuan membuat analisis performa kinerja algoritma Xception dan VGG16 untuk pengenalan lebah pollen-bearing. Pada hasil eksperimen diatas model VGG16 dengan fine_tuning memperoleh nilai akurasi testing terbaik yaitu 83.33%. Begitu juga dengan nilai Cohens kappa, F1_score, ROC AUC, Precision, dan Recall terbaik diperoleh model VGG16 dengan fine_tuning. Untuk model Xception terbaik diperoleh dengan tanpa fine tuning yaitu sebesar 72.22%. pada hasil eksperimen ini diperoleh kesimpulan pre-trained model VGG16 dengan fine_tuning lebih cocok digunakan pada dataset bee_pollen dibandingkan dengan model Xception baik dengan fine_tuning ataupun tanpa fine_tuning.

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Towards Precise Recognition of Pollen Bearing Bees by Convolutional Neural Networks

Cited by 3 publications

References 14 publications

FPGA Implementation of a Convolutional Neural Network and Its Application for Pollen Detection upon Entrance to the Beehive

FPGA Implementation of a Convolutional Neural Network and Its Application for Pollen Detection upon Entrance to the Beehive

A Novel Convolutional Neural Network Architecture for Pollen-Bearing Honeybee Recognition

Perbandingan Algoritma Xception dan VGG16 Untuk Pengenalan Lebah Pollen-Bearing

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