Deep learning-based classification models for beehive monitoring

Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It

Honeybees (Apis mellifera L.) are important for agriculture and ecosystems; however, they are threatened by the changing climate. In order to adapt and respond to emerging difficulties, beekeepers require the ability to continuously monitor their beehives. To carry out this, the utilization of advanced machine learning techniques proves to be an exceptional tool. This review provides a comprehensive analysis of the available research on the different applications of artificial intelligence (AI) in beekeeping that are relevant to climate change. Presented studies have shown that AI can be used in various scientific aspects of beekeeping and can work with several data types (e.g., sound, sensor readings, images) to investigate, model, predict, and help make decisions in apiaries. Research articles related to various aspects of apiculture, e.g., managing hives, maintaining their health, detecting pests and diseases, and climate and habitat management, were analyzed. It was found that several environmental, behavioral, and physical attributes needed to be monitored in real-time to be able to understand and fully predict the state of the hives. Finally, it could be concluded that even if there is not yet a full-scale monitoring method for apiculture, the already available approaches (even with their identified shortcomings) can help maintain sustainability in the changing apiculture.

A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet

Diatoms are a type of algae with many species. Accurate and quick classification of diatom species is important in many fields, such as water quality analysis and weather change forecasting. Traditional methods for diatom classification, specifically morphological taxonomy and molecular detection, are time-consuming and may not provide satisfactory performance. However, in recent years, deep learning has demonstrated impressive performance in this task, just like other image classification problems. On the other hand, networks with more layers do not guarantee increased accuracy. While increasing depth can be useful in capturing complex features and patterns, it also introduces challenges such as vanishing gradients, overfitting, and optimization challenges. Therefore, in our work, we propose DiatomNet, a lightweight convolutional neural network (CNN) model that can classify diatom species accurately while requiring low computing resources. A recently introduced dataset consisting of 3,027 diatom images and 68 diatom species is used to train and evaluate the model. The model is compared with well-known and successful CNN models (i.e., AlexNet, GoogleNet, Inceptionv3, ResNet18, VGG16, and Xception) and their customized versions obtained with transfer learning. The comparison is based on several success metrics: accuracy, precision, recall, F-measure, number of learnable parameters, training, and prediction time. Eventually, the experimental results reveal that DiatomNet outperforms the other models regarding all metrics with just a few exceptions. Therefore, it is a lightweight but strong candidate for diatom classification tasks.

DY-RetinaNet Based Identification of Common Species at Beehive Nest Gates

Target detection at the hive gate of a beehive can be used to effectively monitor invasive beehive species. However, in the natural environment, there is often a multi-target and multi-scale problem at the hive gate, making it difficult for beekeepers to accurately detect the internal state of the hive. (1) To solve the above problems, this paper proposes an improved RetinaNet target detection network, DY-RetinaNet, for the identification of common species at the hive doors of beehives in natural environments, i.e., Chinese bees, wasps, and cockroaches. (2) First, to solve the multi-target multi-scale problem presented in this paper, we propose replacing the FPN layer in the initial model RetinaNet with a symmetric structure BiFPN layer consisting of a feature pyramid, which allows the model to better balance the feature information of different scales. Then, for the loss function, using CIOU loss instead of smooth L1 loss makes the network more accurate for small target localization at multiple scales. Finally, the dynamic head framework is added after the model backbone network, due to the benefits of its multi-attention mechanism, which makes the model more concerned with multi-scale recognition in a multi-target scenario. (3) The experimental results of the homemade dataset show that DY-RetinaNet has the best network performance, compared to the initial model RetinaNet, when the backbone network is ResNet-101-BiFPN, and the mAP value of DY-RetinaNet is 97.38%. Compared with the initial model, the accuracy is improved by 6.77%. The experimental results from the public dataset MSCOCO 2017 show that DY-RetinaNet is better than the existing commonly used target-detection algorithms, such as SSD, YOLOV3, Faster R-CNN, Mask R-CNN, FCOS, and ExtremeNet. These results verify that the model has strong recognition accuracy and generalization ability for multi-target multi-scale detection.