Recognition of Pollen-Bearing Bees from Video Using Convolutional Neural Network

Rodríguez, Iván; Megret, Rémi; Acuña, Edgar; Agosto‐Rivera, José L.; Giray, Tuğrul

doi:10.1109/wacv.2018.00041

Cited by 39 publications

(44 citation statements)

References 11 publications

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“…However, we have to admit that a broader experimental study considering the dependence on the size of the databases has not been performed and presented here. [36] Proposed Model Hatipoglu, N., & Bilgin, G. [37] Proposed Model Prahs,Philipp, et al [38] Proposed Model…”

Section: Discussionmentioning

confidence: 99%

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Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

et al. 2020

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Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow.

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Section: Discussionmentioning

confidence: 99%

“…Another comparative analysis is done by applying the proposed model on different datasets, as shown in Table 3. The first dataset is a pollinating bees' dataset [36]. The dataset has been created from videos captured at the entrance of a bee colony in June 2017.…”

Section: A Deep Learning Approachmentioning

confidence: 99%

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

et al. 2020

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“…Classification of events is done using the geometry of the track (length, direction, starting point and ending point) into entrance and exit [4]. Detected bees are also classified into pollen and nonpollen bearing bees using a convolutional neural network [1] applied to geometrically compensated images obtained using the estimated pose. The complete pipeline runs at an average of 5 fps using one NVIDIA P100 GPU with 12GB RAM.…”

Section: Discussionmentioning

confidence: 99%

“…The videos of behavior have been acquired using the setup described in more details in [1] and illustrated in Figure 1. The videos capture the entrance ramp of the colony, showing entrances and exits, as well as behaviors on the ramp such as fanning or guarding.…”

Section: Overviewmentioning

confidence: 99%

LabelBee

Megret

Rodríguez

Ford

et al. 2019

Proceedings of the Conference on Artificial Intelligence for Data Discovery and Reuse

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The LabelBee system is a web application designed to facilitate the collection, annotation and analysis of large amounts of honeybee behavior data from video monitoring. It is developed as part of NSF BIGDATA project "Large-scale multi-parameter analysis of honeybee behavior in their natural habitat", where we analyze continuous video of the entrance of bee colonies. Due to the large volume of data and its complexity, LabelBee provides advanced Artificial Intelligence and visualization capabilities to enable the construction of good quality datasets necessary for the discovery of complex behavior patterns. It integrates several levels of information: raw video, honeybee positions, decoded tags, individual trajectories and behavior events (entrance/exit, presence of pollen, fanning, etc.). This integration enables the combination of manual and automatic processing by the biologist end-users, who also share and correct their annotation through a centralized server. These annotations are used by the Computer Scientists to create new automatic models, and improve the quality of the automatic modules. The data constructed by this semi-automatized approach can then be exported for the analytic part, which is taking place on the same server using Jupyter notebooks for the extraction and exploration of behavior patterns. CCS CONCEPTS • Applied computing-Life and medical sciences-Computational biology • Information systems-Information systems applications-Multimedia information systems • Human-centered computing-Visualization-Visualization systems and tools

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“…The paper [ 11 ] presents a CNN based approach to bee classification based on whether or not the bees are carrying pollen. Hardware setup for image acquisition is described, methods for bee segmentation are discussed and VGG16, VGG19 and ResNet50 CNN performance is compared to classical classifiers such as KNN, Naive Bayes and SVM.…”

Section: Related Workmentioning

confidence: 99%

Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques

Bilík

Kratochvíla

Ligocki

et al. 2021

Sensors

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The Varroa destructor mite is one of the most dangerous Honey Bee (Apis mellifera) parasites worldwide and the bee colonies have to be regularly monitored in order to control its spread. In this paper we present an object detector based method for health state monitoring of bee colonies. This method has the potential for online measurement and processing. In our experiment, we compare the YOLO and SSD object detectors along with the Deep SVDD anomaly detector. Based on the custom dataset with 600 ground-truth images of healthy and infected bees in various scenes, the detectors reached the highest F1 score up to 0.874 in the infected bee detection and up to 0.714 in the detection of the Varroa destructor mite itself. The results demonstrate the potential of this approach, which will be later used in the real-time computer vision based honey bee inspection system. To the best of our knowledge, this study is the first one using object detectors for the Varroa destructor mite detection on a honey bee. We expect that performance of those object detectors will enable us to inspect the health status of the honey bee colonies in real time.

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Recognition of Pollen-Bearing Bees from Video Using Convolutional Neural Network

Cited by 39 publications

References 11 publications

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

LabelBee

Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques

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