Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It
Abstract
:Simple Summary
Abstract
1. Introduction
2. Apiculture and Its Challenges
2.1. Population Reduction and Distribution
2.2. Genetic Diversity Reduction
2.3. Pest and Disease Occurrence
3. Machine Learning
4. AI Application in Apiculture Studies
4.1. AI in Beekeeping Management/Hive Monitoring
4.2. AI in Bee Health and Disease Monitoring
4.3. AI in Bee’s Habitat and Climate Management
4.4. AI in Subspecies Distribution and Population Management
5. Conclusions and Direction towards Sustainable Agriculture
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | Short Description | Usage in Honeybee Research |
---|---|---|
Artificial Neural Network (ANN) and Neural Networks (NNs) | As computational models inspired by the structure and function of the human brain, ANNs and NNs comprise interconnected nodes, or neurons, arranged in layers. ANNs are renowned for their capacity to discern intricate patterns and relationships within data, rendering them applicable across diverse domains [36]. NNs require less formal statistical training, can detect complex nonlinear relationships between dependent and independent variables, have all possible interactions between predictor variables, and have the availability of multiple training algorithms [37]. | Monitoring of pesticide effect on bee behavior [38]. Modelling the flight activity of workers at the hive entrance [39]. Classification of honey [40]. Unraveling associations between the environment and oxidative stress biomarkers in honeybees [41]. Determining daily performance of colony based on weather [42]. Classifying bee colony acoustic patterns [43]. Characterizing seasonal patterns of colony development [44]. |
Convolutional Neural Network (CNN) | It is widely employed in image and video recognition tasks, which automatically learn relevant features from raw input data, making them highly effective in tasks such as image classification, object detection, and image segmentation [45]. | Estimation of honeybee density in hives [46]. Decoding waggle dances [47]. Honeybee subspecies determination using image recognition for honeybee wing analysis [48]. |
Extremely Randomized Trees (ETs) | A type of ensemble learning method that constructs several decision trees to perform classification or regression tasks, with the aim to provide additional randomness into the process of constructing trees in order to enhance generalization and mitigate overfitting [49]. | Bee sound classification for hives management [50]. Queen bee detection from audio recording [51]. |
Validated Counter-Propagation Artificial Neural Network (CPANN) | A specialized variant of ANNs that integrate elements of counter-propagation networks with validation techniques, and typically comprises two layers: an input layer and a competitive layer. This process enables CPANN to cluster data into meaningful groups or classes based on similarities in input patterns; it also incorporates validation procedures to optimize network performance and enhance generalization capabilities [52]. | Classification models for substances exhibiting acute toxicity for honeybees [53]. |
Gradient Boosting Regressor (GBR) | Mainly used for regression problems, by making predictions using outputs from multiple decision trees. GBR constructs one tree at a time and corrects the errors of the preceding trees [54]. | Identifying factors influencing queen body mass [55]. Prediction of honey harvest [56]. Revealing the relationship between number of bees in the beehive and temperature [57]. |
K-Nearest Neighbor (KNN) | A straightforward ML algorithm utilized for classification and clustering tasks by assessing the proximity of data points to categorize or predict the grouping of individual observations. For each new observation, KNN determines classification by computing its distance from all known observations. The majority class of the K-nearest neighbors then determines the classification outcome [58]. | Discrimination of unifloral honeys [59]. Classifying bee colony acoustic patterns [43]. |
Logistic Regression (LR) | Used for modeling binary or categorical outcomes by predicting the probability of a categorical outcome based on one or more predictor variables. It can be used for both classification and regression problems, but it is more commonly used for classification [60]. | Classifying honeybee sounds with spectrogram features [61]. Classifying bee colony acoustic patterns [43]. |
Long Short-Term Memory (LSTM) | A type of ANN designed to process sequential data by maintaining an internal state or memory. It can handle long time-series data, can avoid vanishing gradient problems, can handle variable-length sequences, has a memory cell that can store and retrieve information, and has gradient flow control [62]. | Detection of queenlessness in beehives [63]. Forecasting sudden drops of temperature in pre-overwintering honeybee colonies [64]. |
Naive Bayes (NB) | NB classifier is based on the Bayes Theorem to generate the predictions for each observation by classifying a sample into a group that is most likely to have its attributes [65]. | Selecting features for honeybee subspecies determination [66]. |
High-Dimensional Discriminant Analysis (HDDA) | Used for discriminant analysis when there are a large number of variables (features) compared to the number of observations (samples) [67]. | Classification of unifloral honey [59]. |
Partial Least Square (PLS) | Enables the comparison of numerous response variables and multiple explanatory variables in a multivariate setting. PLS is a covariance-based statistical method that is commonly known as structural equation modeling or SEM [68]. | Mineral content detection in honey [69] and bee pollen [70]. Identify honey based on its various entomological origins [71]. Honey quality prediction [72]. |
Penalized Discriminant Analysis (PDA) and Shrinkage Discriminant Analysis (SDA) | PDA and SDA are employed in the field of classification and pattern recognition. It is a continuation of Linear Discriminant Analysis (LDA). The primary objective of PDA is to enhance the efficacy of LDA, particularly in scenarios where there is an imbalance between the number of variables (features) and observations (samples), or when the data are affected by multicollinearity [73]. SDA aims to enhance the estimate of the covariance matrix utilized in LDA by implementing a shrinkage strategy on the sample covariance matrix [74]. | Classification of unifloral honey [59]. |
Polynomial Regression Algorithm (PR) | A form of linear regression in which the relationship between the input variable x and the output variable y is modeled as a polynomial and considered to be a special case of linear regression [75]. | Bee foraging behaviors [76]. |
Random Forest (RF) | Based on a group (or forest) of decision trees used to generate the classifications. Decision trees are structures that are based on decision rules to branch out into possibilities and create a path. At the end of the path is the rating assigned to the entry [77]. | Predicting overwintering survival [78]. Predicting honey harvest [56]. Chemical toxicity to honeybee assessment [79]. Classifying bee colony acoustic patterns [43]. |
Support Vector Machine (SVM) | It can be used for classification, regression, or other tasks. It is good for producing high-quality results with interpretability and flexibility; it does not require too much memory, and is effective in high-dimensional spaces [80]. | Classifying bee colony acoustic patterns [43,81]. Detecting bee queen presence [82]. Developing real-time bee counting radar [83]. Discrimination of honeybee subspecies based on wing images [84]. |
Support Vector Regressor (SVR) | The SVR is the regression algorithm of SVM. It can find the best fit line, which is the hyperplane that has the maximum number of points [85]. | Real-time radar for bee count activity [83]. |
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Astuti, P.K.; Hegedűs, B.; Oleksa, A.; Bagi, Z.; Kusza, S. Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It. Insects 2024, 15, 418. https://doi.org/10.3390/insects15060418
Astuti PK, Hegedűs B, Oleksa A, Bagi Z, Kusza S. Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It. Insects. 2024; 15(6):418. https://doi.org/10.3390/insects15060418
Chicago/Turabian StyleAstuti, Putri Kusuma, Bettina Hegedűs, Andrzej Oleksa, Zoltán Bagi, and Szilvia Kusza. 2024. "Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It" Insects 15, no. 6: 418. https://doi.org/10.3390/insects15060418
APA StyleAstuti, P. K., Hegedűs, B., Oleksa, A., Bagi, Z., & Kusza, S. (2024). Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It. Insects, 15(6), 418. https://doi.org/10.3390/insects15060418