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Review

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

1
Centre for Agricultural Genomics and Biotechnology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 4032 Debrecen, Hungary
2
Doctoral School of Animal Science, University of Debrecen, 4032 Debrecen, Hungary
3
Department of Animal Breeding and Reproduction, Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
4
Department of Genetics, Faculty of Biological Sciences, Kazimierz Wielki University, 85-090 Bydgoszcz, Poland
*
Author to whom correspondence should be addressed.
Insects 2024, 15(6), 418; https://doi.org/10.3390/insects15060418
Submission received: 16 May 2024 / Revised: 2 June 2024 / Accepted: 3 June 2024 / Published: 4 June 2024
(This article belongs to the Special Issue Precision Apicultures)

Abstract

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Simple Summary

Worldwide, honeybees (Apis mellifera L.) are involved in pollinating both wild and economically useful plants, while their products are also used by the food and pharmaceutical industries. But currently, apiculture is encountering the adverse effects of global climate change, including more variable rainfall, shifting seasonal precipitation, and increasing temperature averages. These changes threaten the sustainable future of apiculture as these anomalies have already contributed significantly to the economic downturn of the apiculture industry in recent years. In this review, we provide an overview of the current challenges faced by apiculture due to climate change, as well as artificial intelligence (AI) applications in apiculture that can assist to address them. AI has been utilized in various scientific aspects of apiculture, such as managing hives, maintaining health, detecting pests and diseases, monitoring habitats, and managing population distribution. This is achieved by analyzing data objects such as text, audio, images, videos, sensor readings, and numerical values to investigate, model, predict, and make supporting decisions. Several shortcomings of the existing AI application are identified in this review, and the knowledge gaps regarding the development of autonomous intelligent systems for sustainable beekeeping are also highlighted.

Abstract

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.

1. Introduction

The honeybee (Apis mellifera L.) possesses significant economic value in both agricultural and natural ecosystems due to its role as a natural pollinator. The species’ characteristics and geographic spread are significantly influenced by climatic factors, such as variations in rainfall and temperature across the seasons [1]. Currently, we are witnessing substantial shifts in global climate patterns, and these changes are projected to persist in the future. It is anticipated that these changes will impact the diversity of plants and animals [2], including bees, as has been simulated by many researchers from various climatic conditions under numerous climatic change scenarios [3,4,5]. A study conducted by Pardee et al. [6] highlighted that climate change might induce the shift in distribution and decline in the population of the bee pollinator community, resulting in a shifting of genetic diversity, e.g., in Latin America, it was estimated to be reduced by 65% by 2050 [7]. Furthermore, this situation will lead to further implications of the primary factors contributing to the decline in bee biodiversity, such as habitat loss or fragmentation, the emergence of invasive species, the spread of diseases, and the use of pesticides [8]. Furthermore, bees exhibit certain changing behaviors that help in maintaining homeostatic circumstances in the face of climate change, both at the individual and colony levels. This poses additional challenges for the practice of apiculture management [9].
In order to develop effective management strategies, it is crucial to understand the adaptations that bees experience in response to climate change. Nevertheless, as the worldwide climate change phenomena intensify and become less predictable, comprehending the adverse consequences of climate change and developing an effective plan to alleviate them gets increasingly complicated. Artificial intelligence (AI) and machine learning (ML) could be some of the options. AI refers to the capacity of machines to acquire knowledge from past experiences, adapt to new information, and carry out tasks that resemble human abilities. It also offers innovative opportunities for identifying patterns in a vast amount of unstructured data, including the implementation of self-learning new algorithms [10]. In this review, we compile the recent AI studies in apiculture to help guide how to improve beekeeping management and gather around the cause of sustainable apiculture in the face of climate change.
We conducted a literature review, which aims to offer a comprehensive summary of the present collection of the literature related to AI application in beekeeping without any strict and predefined methodology. However, for easier literature collection, we implemented a systematic search strategy to identify the relevant literature across electronic databases, including Google Scholar, PubMed, Scopus, and Web of Science. The search was conducted without the specification of a time frame. It included variations and combinations of keywords related to artificial intelligence (e.g., “machine learning”, “deep learning”, “neural networks”) and apiculture (e.g., “Apis mellifera”, “beekeeping”, “honeybees”). The Boolean operator “AND” was used to combine search terms appropriately.
The inclusion criteria for studies were as follows: (1) studies focusing on applying artificial intelligence techniques in apiculture practices or beekeeping management; (2) studies examining the impact of artificial intelligence on sustainability outcomes in apiculture, including environmental, economic, and social dimensions; (3) studies published in peer-reviewed journals or conference proceedings; (4) the article is in a form of an original article, not a review. The exclusion criteria were as follows: (1) studies not directly related to artificial intelligence applications in apiculture or sustainability outcomes; (2) studies published in languages other than English; (3) studies lacking full-text availability or access to sufficient data for review.

2. Apiculture and Its Challenges

2.1. Population Reduction and Distribution

The global honeybee population is at risk due to the impacts of climate change, which include more frequent and severe extreme weather events such as heatwaves, droughts, and unpredictable rainfall [11]. Increased temperatures during warm and arid summers can lead to higher mortality rates among bee populations, particularly if the temperatures exceed the thermoregulation threshold specific to each bee species [12]. The genotype and environment significantly affect colony development and adaptation, according to Hatjina et al. [13], as Apis mellifera L. bee colonies found in warmer regions of southern Europe have fewer worker bees compared to colonies in colder regions. Similarly, colonies in colder areas have smaller brood populations, indicating that bees have shorter lifespans in warmer climates and a shorter period of raising broods in colder climates. Moreover, the preceding year’s warmer and drier climatic conditions are linked to a rise in winter honeybee colony mortality.
In addition to honeybee colony mortality, climate imposes a more significant influence on vegetation, hence impacting the honeybee foraging season, colony development, and overall vitality of the colony. Drought reduces soil moisture, causing water stress in plants. Consequently, flower production is diminished, resulting in limited availability of nectar and pollen resources for honeybees [12,14]. For example, a case was reported from Mediterranean areas during the drought season in 2016 and 2017 by Flores et al. [15] and reveals a significant decrease in field availability of food, which in turn increases stress levels owing to food scarcity and ultimately leads to starvation-related deaths of the A. mellifera L. honeybee colonies. Furthermore, they discovered a 15% reduction in the amount of Eucalyptus pollen grains in the honey, accompanied by an increase in the quantity of pollen from drought-tolerant flowers. Consequently, this resulted in a decline in the market value of the honey.

2.2. Genetic Diversity Reduction

Gene flow and, ultimately, shifting subspecies ranges in an area are caused by adaptation to climatic conditions. According to a study conducted in Serbia published by Tanasković et al. [16], it has long been known that warmer regions of the country are occupied mainly by A. m. macedonica and colder regions by A. m. carnica due to its adaptability. However, recent findings indicate that A. m. macedonica is not anymore present in Serbia. Through the examination of 14 microsatellite loci, it has been determined that Serbia now possesses a distinct hybrid honeybee population, resulting in genetic homogeneity and the formation of an admixture population. These conditions are harmful since, in honeybees, a high level of genetic diversity within a colony is necessary to boost its fitness, making it more productive, better able to maintain homeostasis, and less susceptible to disease [17]. Hungarian beekeepers have also documented this type of genetic admixture; in the past, A. m. carnica was considered as their indigenous subspecies. However, mitochondrial DNA and microsatellite analyses revealed a small amount of genetic introgression from other subspecies, including A. m. mellifera and A. m. ligustica [18]. In addition, the hybridization with African bees has become a growing concern for European beekeepers. It is quite alarming, as we can witness the events that occurred on the American continent. In 1956, bees (A. m. scutellata) imported from East Africa began to spread from southern Brazil and hybridize with already established European subspecies, leading to the emergence of a highly invasive and aggressive honeybee hybrid population [19]. Similar incidents can occur in Europe due to the consistent fluctuations in climate and changing patterns of subspecies migration. Evidence was reported in the Iberian Peninsula that shared haplotypes between bees from the African and European lines (M79 and M79a) and was detected using molecular genomics tools [20], while in another case in East–Central Europe, Oleksa et al. [21] reported that 1.64% of their bees had African mitochondrial DNA in their genetic background.
The climatic aspects can affect the distribution of the different Africanized subspecies, as they reported in Argentina; European morphotypes were found in central and southern regions, while the northern region mostly contained bees with African morphotypes [22]. This indicates that the exchange of genes between the two honeybee species is imbalanced, perhaps due to the dominance of African genetic material over European genetic material. Additionally, the African honeybees were more capable of adapting to the climate of concern [23].

2.3. Pest and Disease Occurrence

Parasitic, nonparasitic, omnivorous, and pollen-feeding mites are all possible inhabitants of honeybee colonies, and the prevalence of this risk increases due to unpredictable weather conditions. The honey beekeeping sector is vulnerable to infestation by several mite species, the most economically relevant of which are Varroa destructor, Acarapis woodi, Tropilaelaps clareae [24], Paenibacillus larvae [25], and Nosema ceranae [26]. Several cases and investigations have been documented regarding changing patterns of occurrence of various diseases as a result of global climate change. Beekeepers in Piedmont, northwest Italy, have reported about the high infestation and continuing V. destructor reproduction caused by mild winters, as reported in the study by Vercelli et al. [27]. The investigation conducted by Rowland et al. [28] on the climatic influences affecting prevalent honeybee pathogens in England and Wales indicates a positive correlation between the prevalence of V. destructor and associated diseases and increasing temperatures while suggesting a negative correlation with higher levels of rainfall and wind. As temperatures rose, the likelihood of the sacbrood virus also increased, as well as the chalkbrood disease that is caused by a fungal pathogen. Interestingly, this disease has an inverse correlation with temperature, meaning that it is more likely to occur as temperatures decrease. The modeling of the potential global distribution of the Galleria mellonella pest by Hosni et al. [29] indicates that the annual mean temperature and temperature annual range account for 64.2% and 19% of the distribution of pests, respectively. According to their predictions, the climate anomalies’ phenomena will cause the spread of the event to further locations and result in a more substantial financial impact on the honeybee industry in the future. Another significant threat to honeybee colony health is the small hive beetle, Aethina tumida, originating from southern and Central Africa but progressively spreading to all continents. The species emerged in North Africa and South Europe, and its distribution appears to be linked to climate change. The increasing temperature could potentially foster a favorable habitat for its proliferation [30]. Because of this, a study [31] predicted these climate change effects with the shared socio-economic pathways to see where they will spread with the predicted temperature changes. The countries are strongly encouraged to develop monitoring systems for A. tumida to prevent the further spread of the infection.

3. Machine Learning

Currently, we live in the Fourth Industrial Revolution (4IR) [32], wherein a vast quantity of information is accessible to us via technologies [33]. ML algorithms are effective tools for generating decision trees, rules, or statistics as they are employed to teach machines how to effectively process large databases. ML algorithms utilize a training dataset to generate rules that form a predictive model, which is validated later with a separate test dataset [34]. The outcome of the model will improve with each iteration, as it benefits from the repetition and learning process. The datasets for the algorithms can originate from many sources (e.g., IoT devices, databases) in different forms, and the outcomes of the algorithms can be evaluated using various metrics such as accuracy, precision, and speed. Furthermore, deep learning (DL) is a subset of ML that originated from the artificial neural network (ANN) but has outperformed it and is capable of performing more complex operations [35]. Several ML algorithms and models have been developed and are currently in use, each with its own specific applications in apiculture (Table 1). The following subsections concentrate on some of the primary applications of AI or ML in apiculture that address the issue of climate change. They provide a short and precise description of the experimental results, their interpretation, and the experimental conclusions that may be taken.

4. AI Application in Apiculture Studies

4.1. AI in Beekeeping Management/Hive Monitoring

An important issue in apiculture is the significant loss of bees due to colony collapse disorder (CCD) and the serious consequences of declining bee numbers. It could be attributed to factors such as inadequate nutrition, increased stress from ecto- and endoparasites, elevated bacterial and/or viral loads, and the combined effects of pesticides, which may ultimately be linked to climate change [86]. Comprehending the dynamics of bee colonies is intricate, and relying solely on manual inspections will not yield good outcomes for the beehives. AI technology has facilitated monitoring of beehives through several techniques such as an audio analysis [87], camera-based visual observation [88], monitoring movement [89], and analyzing physical attributes of the hives [19] (Figure 1).
Flores et al. [15] employed the Wbee system to remotely monitor the weight of beehives at fifteen-minute intervals in order to investigate the correlation between environmental conditions and hive weight. The system is structured in a three-tiered hierarchical model and relies on wireless communication. Researchers observed a correlation between decreased weight of beehives and days characterized by low temperatures, cloudiness, and rain. This suggests that the bees had limited access to food during such weather conditions. A more advanced study by Anwar et al. [90] used a hybrid deep learning model (8-sensor system—NB-IoT, LSTM) for time series forecasting and soft sensing to convert the daily variation in hive weight into predictive daily hive weight. Their result is quite sophisticated, with 83.5% of the days having mistakes of less than 25 g per frame, according to the daily estimations.
Kulyukin et al. [91] employed the BeePi system (Utah, United States of America), which monitors beehives using audio by comparing several DL and traditional ML techniques to identify audio samples from microphones positioned about 10 cm above the landing pads of Langstroth beehives. A 30 s audio file was recorded every 15 min for classification using four different ML algorithms: LR; KNN, with a linear kernel one vs. rest (SVM OVR); and RF. LR demonstrated the highest performance among others, indicating that a less complex raw audio CNN yielded the most accurate classification of the audio samples and also showed a high potential for practical use. In another study, Di et al. [87] suggested utilizing the VGGish embedding, a model for audio classification similar to the visual geometry group, in conjunction with the KNN model for audio classification in beehive audio monitoring. Additionally, Zgank [92] introduced a system that uses IoT technology based on acoustic signals to classify bee swarm activity. The system utilized feature extraction techniques such as Mel-frequency cepstral coefficients (MFCCs) and linear predictive coding (LPC) to analyze the input audio signal and monitor this significant occurrence in beehives.
In image classification, using various models of deep learning classification, Berkaya et al. [93] identified different conditions using honeybee photos captured at beehives with Deep Neural Networks (DNNs) and SVM algorithms. A variety of conditions can be identified by the suggested models, including healthy bees, pollen-bearing bees, and abnormalities including ant difficulties, small hive beetles, hive robberies, and Varroa parasites, all with a remarkably high accuracy of 99.07% and a relatively quick classification time. A recent study conducted by Williams et al. [88] examined the enhancement in thermal cameras using machine learning techniques (KNN, neural networks/NNs, RF, and SVM). Despite having inferior baseline specs compared to a competitive optical camera, these cameras were able to attain the same degree of efficiency in monitoring the activity at the entrance of a beehive. The thermal camera provides the benefit of functioning efficiently in all circumstances without requiring adequate lighting conditions. In the case of thermal data, the KNN and NN algorithms were the most efficient, while the feature specificity was the best in the case of SVM and RF. Their analysis confirms that the thermal camera effectively captured and accurately identified the flight. In their subsequent investigation, Williams et al. [83] conducted experiments to evaluate the efficacy of this thermal camera in implementing a real-time radar signal classification system with SVR algorithms for monitoring and quantifying bee activity at the hive’s entrance.
Furthermore, Alves et al. [94] developed DeepBee© software using imaging data, which can identify cells in comb photos and classify their contents into seven categories: eggs, larvae, capped brood, pollen, nectar, honey, and others. The objective is to assist beekeepers in evaluating the quantities of comb cells containing brood and food reserves, allowing the evaluation of the colony’s nutritional and health condition, queen quality, and honey production potential. By employing the Circle Hough Transform and semantic segmentation technique, a cell detection rate of 98.7% was achieved. Among 13 different CNN algorithms evaluated for comb cell classification, MobileNet emerged as the optimal option, achieving a balance between training cost and accuracy, with processing time averaging approximately 9 s per comb image and an F1-Score of 94.3%.

4.2. AI in Bee Health and Disease Monitoring

For effective disease management and mitigation, it is important to comprehend the patterns of dispersion and how climatic conditions impact them. By considering multiple recorded parameters, modeling systems can assist in predicting it. Using the Maximum Entropy model’s algorithm, Hosni et al. [29] forecasted the dispersal of Galleria mellonella pests by building the model using 19 components of bioclimatic data and the reported disease occurrence. The most effective climatological parameters that affect the dispersion of this pest were determined to be the annual temperature range and mean, along with yearly precipitation. Slovenia, Slovakia, France, Italy, Belgium, the United Kingdom, the Mediterranean coast, and a few other countries on other continents were on the list of the high-potential future habitats for this pest. The same research approach and results were employed to investigate the distribution of A. tumida, Galleria mellonella, and Oplostomus haroldi, besides Varroa destructor in Kenya [95] and Tanzania [96]. They forecasted a growing likelihood of suitable habitats for these pests in various regions of these countries in the future.
Monitoring the infestation of pests and determining their level is an important and challenging duty for beekeepers, as early detection is a high determinant in disease management. Research utilizing videos of honeybee behaviors, which are subsequently converted into image patches [97,98,99], was able to detect the parasite infection in beehives. The input photos underwent classification using diverse methodologies, such as the Bayesian theorem, statistical learning theory, and a combination of decision trees, to detect the presence of mites. The outcome revealed a high categorization accuracy exceeding 70% and resulted in time savings of over 50% compared to manually observing mite presence. In another example, Wachowicz et al. [100] propose a method for the real-time monitoring and detection of pests by analyzing a combination of various detection methods (camera-based IoT devices, pre-trained CNN approach) and a cloud data center for monitoring and notification. The study demonstrates a significant enhancement in accuracy, reaching up to 90% of parameters from the video stream of hive conditions using IoT devices, and edge-based V. destructor, by employing a convolutional neural network methodology. However, it is important to clarify that the detection capability of the device is limited to the mites specifically within honeybee cells, and does not extend to the mites present on the bodies of bees. Besides this, Mrozek et al. [101] used another monitoring IoT device with optical recordings (20 pictures from a video stream). The collected data were analyzed with a CNN algorithm, while the resulting information (bee and V. destructor identification) was stored/transferred using a cloud that the beekeeper can reach. The precision (70%) and sensitivity (90%) of the Varroa detection rate were high, as well as the honeybee identification (100% and 70%), which means that this method is also useable to prevent a strong infection of the colonies. Some other multi-sensor bee health monitoring systems have also been developed, such as IndusBee4.0 [102] and the BeePi system [103].
An ML model can also accurately assess and forecast temperature declines within honeybee colonies, a crucial determinant of colony well-being. In research by Braga et al. [64], they measured six aspects of the hive, internal temperature, internal humidity, mean fanning, mean noise, mass, and external temperature, with the Arnia system that has three sensors inside the hive: temperature, humidity, and sound sensors; besides these, there is a digital scale under the hive. The gathered data were analyzed using an ML model that employed an LSTM technique. This proposed remote hive monitoring system could predict the temperature one day in advance, with a 0.5% root mean square error (RMSE).
A study by Robles-Guerrero et al. [43] compared five different ML models, to be able to automatically assess the health status of the hive, by analyzing their acoustic data. The following were the mostly used models: the KNN, the LR, the NN, the RF, and the SVM. These are simpler than the DL models, resulting in faster and easier training, but still require computer resources. They gathered acoustic data (by Rpi microphones) from five Carniola honeybee colonies, of which two were strong colonies, two were weaker, and one colony was queenless with the lowest bee population. It was determined that NN and SVM were the optimal alternatives due to their efficient classification time and excellent performance metrics.

4.3. AI in Bee’s Habitat and Climate Management

In the field of apiculture, bees and their interactions with the natural environment are integrated into social–ecological systems. Gaining a comprehensive understanding of the many interrelationships within this system has become more important for effective environmental management and the implementation of innovative interdisciplinary methods that promote sustainable beekeeping [104]. Braga et al. [98] proposed a clustering and classification algorithm (Naive Bayes, KNN, and RF) to detect seasonal patterns in honeybee behavior. The weekly and monthly meteorological data, along with the beehive weight data, were combined to detect the occurrence of swarming, determine the optimal moment for seasonal management, and assess the prevalence of pathologies. In the classification stage, they discovered six distinct seasonal honeybee patterns, each with hit rates reaching as high as 99.67%. Later, Patel et al. [105], with more complex data, utilized a spatially explicit modeling technique, a machine learning algorithm, and an agent-based model to simulate the migration of beehives in relation to the geographic distribution of bee food supply in western Australian apiculture. They effectively illustrated the variations in spatial distributions of the primary bee food species, both on a seasonal and monthly basis, as well as the overall species diversity. Based on that, they predicted the future migration pattern and recommended a change in the hives towards the east part.
It is well known that bees are not only found in their natural habitats, but they are also utilized for pollination, such as in crop fields. This is why beekeepers relocate their honeybees to these areas. However, there is an ongoing disagreement between farmers and beekeepers around the world. One of the reasons is due to frequent bee poisoning (e.g., neonicotinoids), which is usually caused by using sprays before the end of the foraging activity of the bees, leading to significant losses to the beekeeping community every year [106]. Therefore, it is important to accurately forecast the remaining duration of time that bees allocate to foraging in the fields. To overcome this issue, Torresani et al. [107] developed a non-invasive IoT-based machine learning model that considered real-time bee activity, weather, and sunset time to forecast this foraging time. The researchers demonstrated that the GBR was the best suitable regression model for effectively differentiating between foraging and other behaviors in bees. This model can be utilized by farmers to determine optimal spraying timings that are safe for honeybees. Another study [76] investigated the periods of time when bees were collecting food, measuring the number of bees foraging every half hour between 5:00 am and 4:00 pm. The aim of this was not only to determine the number of foraging insects but also the relationship between the number of these bees and the time of the day. A polynomial regression algorithm was used for data processing in this case as part of the machine learning process. The generated model was suitable for testing the inter-day variability of the data obtained, and the gained conclusions can be integrated into future research on bees as well as being possibly applicable in the field.
In a study, real colored Red–Green–Blue (RGB) images were taken by an Unmanned Aerial Vehicle (UAV) to measure flower cover and diversity by association, bee abundance, and diversity. There, Torresani et al. [108] discovered a standardized, large-scale, and cost-effective way to monitor bee habitat remotely (e.g., ‘flower cover data’). They suggested that optical pictures with a better spatial resolution yield more accurate results when using RF machine learning algorithms instead of NN and SVM. The environment of bees can influence the composition of the royal jelly they produce, as demonstrated in scientific research released in 2023 [105]. Because of this, ML methods can even be used to monitor the quality of royal jelly production, as demonstrated in this study. Here, they showed that combining stable isotopes with artificial ANN models (excellent accuracy, sensitivity, and specificity) and considering the unique proven correlation between stable isotopes and environmental factors (temperature, precipitation, sunlight) can provide promising ideas for monitoring the authenticity of royal jelly.

4.4. AI in Subspecies Distribution and Population Management

There are different kinds of A. mellifera L. subspecies, such as previously mentioned A. m. ligustica (Italian honeybee), A. m. carnica (Carniolan honeybee), A. m. mellifera (dark European honeybee), and A. m. scutellata (African honeybee). New methods based on genetics can help to study their geographic distribution more accurately, but it is still mainly performed based on morphometry [22]. Conventional approaches of taxonomic examination are laborious and time-consuming, encouraging people to seek a more efficient solution. In a publication by De Nart et al. [48], they researched the use of AI with ML techniques based on CNN to recognize honeybee subspecies. The research was conducted using four CNN models, utilizing a collection of 9887 wing pictures from seven subspecies and the commonly used Buckfast hybrid. From them, the Inception ResNet V2 performed the best (higher than 98% for accuracy, precision, and specificity), which also surpassed the performance of the traditional method. This demonstrated that an automatic image recognition (with only wing pictures) and ML technology is definitely a promising solution for bee species recognition, promoting the preservation of the biodiversity of honeybee species and to preserve pure lines for the market.
With a similar aim, Rodrigues et al. [84] developed DeepWings© software, which analyzed 19 landmarks in a right forewing image, to make a fully automated morphometric-based assessment of honeybee subspecies. They started to work with a smaller but richer dataset including 7634 forewing images, containing pictures of 26 subspecies. In this study, they also used CNN as a wing detector, but for detecting the 19 features, they applied U-Net DLtool, and SVM to classify subspecies. Eventually, they successfully processed 10 images within a time frame of 14 s, which is the minimum acceptable duration. Initially, the accuracy rate was 86.6%. However, after training with just five subspecies, the precision improved to 95.8%. It is possible that this percentage might have further risen with further training over time. This software has 19 comprehensive features, which can also make a significant addition to other similar investigations. This same software was used in another study [109], with 14,816 wing images from 2601 colonies that belonged to three Apis mellifera subspecies. The results were somewhat similar to the first accuracy of the previous paper, with 89.7% for the A.m. iberiensis (Iberian honeybee), and 88.3% for the Carniolan honeybee, but in the case of the dark honeybee, they only received a 41.1% match. It has been demonstrated that while the DeepWings© software is a valuable tool for identifying subspecies, it can still cause confusion in identifying subspecies, particularly when the honeybees have genetic impurities.
In addition to a remote optical analysis, acoustic signals can also be utilized for population management by automating the identification of colonies that are ready to swarm. Dimitrios et al. [110] made a comparison between three different classification algorithms: the KNN and SVM, and their new U-Net CNN. The acoustic data that were studied from five colonies, for 5 months, twice a day, for 6–6 h, were acquired by IoT devices specifically designed for this research, which include microphones, temperature sensors, and humidity sensors. Among the various ML models used, KNN and SVM demonstrated the highest accuracy in identifying early and late swarming. However, when it came to early detection specifically, SVM outperformed the rest, with the U-Net CNN method performing similarly.
Certain research endeavors to build a hybrid insect society, in which honeybees and autonomous robots engage in mutual interaction. In order to carry out this, the robots must be fine-tuned using evolutionary algorithms, which necessitate the evaluation of population density and behavioral data. To be able to carry out this, Salem et al. [111] used Combined Actuator Sensor Units (CASUs), which include different sensors, and could communicate with the bees through their heat, movement, etc. They used three new algorithms (J48 Decision Tree—C4.5 based on ID3; JRip Rules Classifier—RIPPER; PART—developed version of C4.5 and RIPPER algorithms) with different setups to learn from the detected information. At the end of the study, the PART model performed the best in both accuracy and ruleset size, while the worst was the JRip. With this, they were able to estimate the bee density accurately. This method can be an option to be used in other future experiments with bee–robot interactions, allowing for control over the colonies.

5. Conclusions and Direction towards Sustainable Agriculture

This review provides an in-depth survey of techniques based on AI and ML that are being used to overcome the difficulties that beekeepers face in coping with climate change. In the current era of the Fourth Industrial Revolution, machine learning algorithms have become increasingly accessible. These algorithms undergo continual training using the data they receive, and they have a diverse variety of applications. Due to this capability, they are able to process a wide range of data, as demonstrated in the setting of apiaries. These data can encompass various factors such as temperature, sound, pictures, video, weather conditions outside the hive, humidity, air circulation, weight, bee activity, and numerous other variables. Hence, the data can be utilized for the purpose of training and refining algorithms, or for evaluating models that have already been validated and approved. The data in the examined research were often gathered using a non-invasive technology based on the Internet of Things (IoT) or an instrument equipped with a specialized sensor or were derived from an existing dataset.
When comparing methodologies, SVM and NN algorithms are frequently utilized and generally considered the most suitable, with CNN being one among them. The NN models usually had an accuracy of over 95%, and they were used mostly in studies too. Besides these, they are the basis for DL technologies. When the ideal database had been discovered and the logarithm was effectively chosen and tested, numerous investigations employed a completely automated monitoring system utilizing ML, such as BeePi (monitoring beehives through audio), the DeepBee© system (monitoring the quality of combs by pictures), and the DeepWings© software (monitoring the morphology by pictures). Therefore, the ML tools are currently functional, albeit not as a comprehensive monitoring technology of the apiculture, due to their ongoing development. These methods can help in maintaining sustainability in the hives despite our changing environment, by monitoring the colonies for a possible early intervention of the beekeeper.
Despite the numerous approaches developed by researchers, the continuous advancement in technology necessitates the processing of larger volumes of data within increasingly shorter timeframes. According to the studied publications, it has been seen that in some cases, the amount of data being processed is still low, and there are still inaccuracies in certain algorithms, especially with genetic purity. Thus, this can provide a challenge as increasingly intricate approaches necessitate the utilization of many algorithms for ML-based solutions (e.g., CNN and SVN in DeepWings software). In addition, the training dataset needs to be enriched and augmented to allow the algorithm to start with a sufficient reference, thus achieving faster process time, higher precision, and minimal computational consumption in the first use. Consequently, it is necessary to enhance and optimize the monitoring devices in order to obtain a wide range of data, enabling a comprehensive evaluation of the colonies. But in the end, every ML monitor system development needs to be tested faster in farm-setting conditions preferably in an apiary to ensure its accessibility and applicability to the beekeepers.

Author Contributions

Conceptualization, S.K.; methodology, P.K.A. and B.H.; writing—original draft, P.K.A., B.H., A.O. and S.K.; writing—review and editing, A.O., Z.B. and S.K.; visualization, B.H.; supervision, S.K.; acquisition, S.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

P.K.A. received support from the Tempus Public Foundation under the Stipendium Hungaricum Programme. Supported by University of Debrecen Program for Scientific Publication.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Khalifa, S.A.M.; Elshafiey, E.H.; Shetaia, A.A.; El-Wahed, A.A.A.; Algethami, A.F.; Musharraf, S.G.; AlAjmi, M.F.; Zhao, C.; Masry, S.H.D.; Abdel-Daim, M.M.; et al. Overview of Bee Pollination and Its Economic Value for Crop Production. Insects 2021, 12, 688. [Google Scholar] [CrossRef] [PubMed]
  2. Astuti, P.K.; Ayoob, A.; Strausz, P.; Vakayil, B.; Kumar, S.H.; Kusza, S. Climate change and dairy farming sustainability; A causal loop paradox and its mitigation scenario. Heliyon 2024, 10, e25200. [Google Scholar] [CrossRef] [PubMed]
  3. Tabor, J.A.; Koch, J.B. Ensemble Models Predict Invasive Bee Habitat Suitability Will Expand under Future Climate Scenarios in Hawai’i. Insects 2021, 12, 443. [Google Scholar] [CrossRef] [PubMed]
  4. Martínez-López, O.; Koch, J.B.; Martínez-Morales, M.A.; Navarrete-Gutiérrez, D.; Enríquez, E.; Vandame, R. Reduction in the potential distribution of bumble bees (Apidae: Bombus) in Mesoamerica under different climate change scenarios: Conservation implications. Glob. Chang. Biol. 2021, 27, 1772–1787. [Google Scholar] [CrossRef]
  5. Lanner, J.; Dubos, N.; Geslin, B.; Leroy, B.; Hernández-Castellano, C.; Dubaić, J.B.; Bortolotti, L.; Calafat, J.D.; Ćetković, A.; Flaminio, S.; et al. On the road: Anthropogenic factors drive the invasion risk of a wild solitary bee species. Sci. Total Environ. 2022, 827, 154246. [Google Scholar] [CrossRef] [PubMed]
  6. Pardee, G.L.; Griffin, S.R.; Stemkovski, M.; Harrison, T.; Portman, Z.M.; Kazenel, M.R.; Lynn, J.S.; Inouye, D.W.; Irwin, R.E. Life-history traits predict responses of wild bees to climate variation. Proc. R. Soc. B-Biol. Sci. 2022, 289, 20212697. [Google Scholar] [CrossRef] [PubMed]
  7. Imbach, P.; Fung, E.; Hannah, L.; Navarro-Racines, C.E.; Roubik, D.W.; Ricketts, T.H.; Harvey, C.A.; Donatti, C.I.; Läderach, P.; Locatelli, B.; et al. Coupling of pollination services and coffee suitability under climate change. Proc. Natl. Acad. Sci. USA 2017, 114, 10438–10442. [Google Scholar] [CrossRef]
  8. McCabe, L.M.; Aslan, C.E.; Cobb, N.S. Decreased bee emergence along an elevation gradient: Implications for climate change revealed by a transplant experiment. Ecology 2022, 103, e03598. [Google Scholar] [CrossRef]
  9. Maebe, K.; Hart, A.F.; Marshall, L.; Vandamme, P.; Vereecken, N.J.; Michez, D.; Smagghe, G. Bumblebee resilience to climate change, through plastic and adaptive responses. Glob. Chang. Biol. 2021, 27, 4223–4237. [Google Scholar] [CrossRef]
  10. Leal Filho, W.; Wall, T.; Rui Mucova, S.A.; Nagy, G.J.; Balogun, A.-L.; Luetz, J.M.; Ng, A.W.; Kovaleva, M.; Safiul Azam, F.M.; Alves, F.; et al. Deploying artificial intelligence for climate change adaptation. Technol. Forecast. Soc. Chang. 2022, 180, 121662. [Google Scholar] [CrossRef]
  11. Abou-Shaara, H.F.; Al-Ghamdi, A.A.; Mohamed, A.A. A Suitability Map for Keeping Honey Bees under Harsh Environmental Conditions Using Geographical Information System. World Appl. Sci. J. 2013, 22, 1099–1105. [Google Scholar] [CrossRef]
  12. Gajardo-Rojas, M.; Muñoz, A.A.; Barichivich, J.; Klock-Barría, K.; Gayo, E.M.; Fontúrbel, F.E.; Olea, M.; Lucas, C.M.; Veas, C. Declining honey production and beekeeper adaptation to climate change in Chile. Prog. Phys. Geogr. Earth Environ. 2022, 46, 737–756. [Google Scholar] [CrossRef]
  13. Hatjina, F.; Costa, C.; Büchler, R.; Uzunov, A.; Drazic, M.; Filipi, J.; Charistos, L.; Ruottinen, L.; Andonov, S.; Meixner, M.D.; et al. Population dynamics of European honey bee genotypes under different environmental conditions. J. Apic. Res. 2014, 53, 233–247. [Google Scholar] [CrossRef]
  14. Descamps, C.; Quinet, M.; Jacquemart, A.-L. The effects of drought on plant–pollinator interactions: What to expect? Environ. Exp. Bot. 2021, 182, 104297. [Google Scholar] [CrossRef]
  15. Flores, J.M.; Gil-Lebrero, S.; Gámiz, V.; Rodríguez, M.I.; Ortiz, M.A.; Quiles, F.J. Effect of the climate change on honey bee colonies in a temperate Mediterranean zone assessed through remote hive weight monitoring system in conjunction with exhaustive colonies assessment. Sci. Total Environ. 2019, 653, 1111–1119. [Google Scholar] [CrossRef] [PubMed]
  16. Tanasković, M.; Erić, P.; Patenković, A.; Erić, K.; Mihajlović, M.; Tanasić, V.; Kusza, S.; Oleksa, A.; Stanisavljević, L.; Davidović, S. Further Evidence of Population Admixture in the Serbian Honey Bee Population. Insects 2022, 13, 180. [Google Scholar] [CrossRef]
  17. Chen, C.; Wang, H.; Liu, Z.; Chen, X.; Tang, J.; Meng, F.; Shi, W. Population Genomics Provide Insights into the Evolution and Adaptation of the Eastern Honey Bee (Apis cerana). Mol. Biol. Evol. 2018, 35, 2260–2271. [Google Scholar] [CrossRef] [PubMed]
  18. Péntek-Zakar, E.; Oleksa, A.; Borowik, T.; Kusza, S. Population structure of honey bees in the Carpathian Basin (Hungary) confirms introgression from surrounding subspecies. Ecol. Evol. 2015, 5, 5456–5467. [Google Scholar] [CrossRef]
  19. Pinto, M.A.; Sheppard, W.S.; Johnston, J.S.; Rubink, W.L.; Coulson, R.N.; Schiff, N.M.; Kandemir, I.; Patton, J.C. Honey Bees (Hymenoptera: Apidae) of African Origin Exist in Non-Africanized Areas of the Southern United States: Evidence from Mitochondrial DNA. Ann. Entomol. Soc. Am. 2007, 100, 289–295. [Google Scholar] [CrossRef]
  20. Chávez-Galarza, J.; Garnery, L.; Henriques, D.; Neves, C.J.; Loucif-Ayad, W.; Jonhston, J.S.; Pinto, M.A. Mitochondrial DNA variation of Apis mellifera iberiensis: Further insights from a large-scale study using sequence data of the tRNAleu-cox2 intergenic region. Apidologie 2017, 48, 533–544. [Google Scholar] [CrossRef]
  21. Oleksa, A.; Kusza, S.; Tofilski, A. Mitochondrial DNA Suggests the Introduction of Honeybees of African Ancestry to East-Central Europe. Insects 2021, 12, 410. [Google Scholar] [CrossRef] [PubMed]
  22. Porrini, L.P.; Quintana, S.; Brasesco, C.; Maggi, M.D.; Porrini, M.P.; Garrido, M.P.; Fernández Iriarte, P.J.; Eguaras, M.J. Current Genetic Diversity of Managed and Commercially Produced Apis Mellifera Colonies in Argentina Inferred by Wing Geometric Morphometrics and COI-COII mtDNA Locus. Apidologie 2022, 53, 61. [Google Scholar] [CrossRef]
  23. Guzmán-Novoa, E.; Benítez, A.C.; Montaño, L.G.E.; Novoa, G.G. Colonización, impacto y control de las abejas melíferas africanizadas en México. Vet. Mex. 2011, 42, 149–178. [Google Scholar]
  24. Hristov, P.; Shumkova, R.; Palova, N.; Neov, B. Factors Associated with Honey Bee Colony Losses: A Mini-Review. Vet. Sci. 2020, 7, 166. [Google Scholar] [CrossRef] [PubMed]
  25. Voulgari-Kokota, A.; Steffan-Dewenter, I.; Keller, A. Susceptibility of Red Mason Bee Larvae to Bacterial Threats Due to Microbiome Exchange with Imported Pollen Provisions. Insects 2020, 11, 373. [Google Scholar] [CrossRef]
  26. Lourenço, A.P.; Guidugli-Lazzarini, K.R.; de Freitas, N.H.A.; Message, D.; Bitondi, M.M.G.; Simões, Z.L.P.; Teixeira, É.W. Immunity and physiological changes in adult honey bees (Apis mellifera) infected with Nosema ceranae: The natural colony environment. J. Insect Physiol. 2021, 131, 104237. [Google Scholar] [CrossRef] [PubMed]
  27. Vercelli, M.; Novelli, S.; Ferrazzi, P.; Lentini, G.; Ferracini, C. A Qualitative Analysis of Beekeepers’ Perceptions and Farm Management Adaptations to the Impact of Climate Change on Honey Bees. Insects 2021, 12, 228. [Google Scholar] [CrossRef] [PubMed]
  28. Rowland, B.W.; Rushton, S.P.; Shirley, M.D.F.; Brown, M.A.; Budge, G.E. Identifying the climatic drivers of honey bee disease in England and Wales. Sci. Rep. 2021, 11, 21953. [Google Scholar] [CrossRef] [PubMed]
  29. Hosni, E.M.; Al-Khalaf, A.A.; Nasser, M.G.; Abou-Shaara, H.F.; Radwan, M.H. Modeling the Potential Global Distribution of Honeybee Pest, Galleria mellonella under Changing Climate. Insects 2022, 13, 484. [Google Scholar] [CrossRef]
  30. Le Conte, Y.; Navajas, M. Climate Change: Impact on Honey Bee Populations and Diseases. Rev. Sci. Tech. 2008, 27, 499–510. [Google Scholar]
  31. Jamal, Z.A.; Abou-Shaara, H.F.; Qamer, S.; Alhumaidi Alotaibi, M.; Ali Khan, K.; Fiaz Khan, M.; Amjad Bashir, M.; Hannan, A.; AL-Kahtani, S.N.; Taha, E.-K.A.; et al. Future Expansion of Small Hive Beetles, Aethina Tumida, towards North Africa and South Europe Based on Temperature Factors Using Maximum Entropy Algorithm. J. King Saud Univ. Sci. 2021, 33, 101242. [Google Scholar] [CrossRef]
  32. Ślusarczyk, B. Industry 4.0—Are we ready? Pol. J. Manag. Stud. 2018, 17, 232–248. [Google Scholar] [CrossRef]
  33. Sarker, I.H.; Kayes, A.S.M.; Badsha, S.; Alqahtani, H.; Watters, P.; Ng, A. Cybersecurity Data Science: An Overview from Machine Learning Perspective. J. Big Data 2020, 7, 41. [Google Scholar] [CrossRef]
  34. Zhang, Y.; Li, Q.; Xin, Y. Research on Eight Machine Learning Algorithms Applicability on Different Characteristics Data Sets in Medical Classification Tasks. Front. Comput. Neurosci. 2024, 18, 1345575. [Google Scholar] [CrossRef]
  35. Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput. Sci. 2021, 2, 160. [Google Scholar] [CrossRef] [PubMed]
  36. Zhang, H.; Li, D. Naïve Bayes Text Classifier. In Proceedings of the IEEE International Conference on Granular Computing (GRC) 2007, Fremont, CA, USA, 2–4 November 2007; p. 708. [Google Scholar] [CrossRef]
  37. Prieto, A.; Prieto, B.; Ortigosa, E.M.; Ros, E.; Pelayo, F.; Ortega, J.; Rojas, I. Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing 2016, 214, 242–268. [Google Scholar] [CrossRef]
  38. Wang, M.; Tausch, F.; Schmidt, K.; Diehl, M.; Knaebe, S.; Bargen, H.; Materne, L.; Groeneveld, J.; Grimm, V. Honeybee pollen but not nectar foraging greatly reduced by neonicotinoids: Insights from AI and simulation. Comput. Electron. Agric. 2024, 221, 108966. [Google Scholar] [CrossRef]
  39. Devillers, J.; Doré, J.C.; Tisseur, M.; Cluzeau, S.; Maurin, G. Modelling the flight activity of Apis mellifera at the hive entrance. Comput. Electron. Agric. 2004, 42, 87–109. [Google Scholar] [CrossRef]
  40. Zuluaga-Domínguez, C.M.; Nieto-Veloza, A.; Quicazán-de-Cuenca, M. Classification of Colombian honeys by electronic nose and physical-chemical parameters, using neural networks and genetic algorithms. J. Apic. Res. 2018, 57, 145–152. [Google Scholar] [CrossRef]
  41. La Porta, G.; Magara, G.; Goretti, E.; Caldaroni, B.; Dörr, A.J.M.; Selvaggi, R.; Pallottini, M.; Gardi, T.; Cenci-Goga, B.T.; Cappelletti, D.; et al. Applying Artificial Neural Networks to Oxidative Stress Biomarkers in Forager Honey Bees (Apis mellifera) for Ecological Assessment. Toxics 2023, 11, 661. [Google Scholar] [CrossRef]
  42. Bayir, R.; Albayrak, A. The Determination of the Developments of Beehives via Artificial Neural Networks. Teh. Vjesn. 2018, 25, 553–557. [Google Scholar] [CrossRef]
  43. Robles-Guerrero, A.; Saucedo-Anaya, T.; Guerrero-Mendez, C.A.; Gómez-Jiménez, S.; Navarro-Solís, D.J. Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources. Sensors 2023, 23, 460. [Google Scholar] [CrossRef] [PubMed]
  44. Maciel, F.A.O.; Coelho Da Silva, T.L.; Braga, A.R.; Freitas, B.M.; Xavier, R.M.; Gomes, D.G. Data mining to characterize seasonal patterns of Apis mellifera honey bee colonies. In Proceedings of the SBSI’18: XIV Brazilian Symposium on Information Systems, Caxias do Sul, Brazil, 4–8 June 2018; ACM International Conference Proceeding Series. Association for Computing Machinery: New York, NY, USA, 2018. [Google Scholar] [CrossRef]
  45. Zhang, S. Challenges in KNN Classification. IEEE Trans. Knowl. Data Eng. 2022, 34, 4663–4675. [Google Scholar] [CrossRef]
  46. Luneckas, T.; Luneckas, M.; Salem, Z.; Szopek, M.; Schmickl, T. Convolutional Neural Network for Honeybee Density Estimation. In Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, Canberra, Australia, 1–4 December 2020; pp. 2558–2566. [Google Scholar] [CrossRef]
  47. Dormagen, D.M.; Wild, B.; Wario, F.; Landgraf, T. Machine learning reveals the waggle drift’s role in the honey bee dance communication system. PNAS Nexus 2023, 2, pgad275. [Google Scholar] [CrossRef] [PubMed]
  48. De Nart, D.; Costa, C.; Di Prisco, G.; Carpana, E. Image Recognition Using Convolutional Neural Networks for Classification of Honey Bee Subspecies. Apidologie 2022, 53, 5. [Google Scholar] [CrossRef]
  49. Geurts, P.; Ernst, D.; Wehenkel, L. Extremely randomized trees. Mach. Learn. 2006, 63, 3–42. [Google Scholar] [CrossRef]
  50. Phan, T.-T.-H.; Nguyen, H.-D.; Nguyen, D.-D. Evaluation of Feature Extraction Methods for Bee Audio Classification. In Intelligence of Things: Technologies and Applications; Lecture Notes on Data Engineering and Communications Technologies; Springer: Berlin/Heidelberg, Germany, 2022; Volume 148, pp. 194–203. [Google Scholar] [CrossRef]
  51. Ho, H.-T.; Pham, M.-T.; Tran, Q.-D.; Pham, Q.-H.; Phan, T.-T.-H. Evaluating Audio Feature Extraction Methods for Identifying Bee Queen Presence. In Proceedings of the 12th International Symposium on Information and Communication Technology 2023, Ho Chi Minh City, Vietnam, 7–8 December 2023; pp. 93–100. [Google Scholar] [CrossRef]
  52. Ballabio, D.; Vasighi, M.; Consonni, V.; Kompany-Zareh, M. Genetic Algorithms for architecture optimisation of Counter-Propagation Artificial Neural Networks. Chemom. Intell. Lab. Syst. 2011, 105, 56–64. [Google Scholar] [CrossRef]
  53. Venko, K.; Drgan, V.; Novič, M. Classification models for identifying substances exhibiting acute contact toxicity in honeybees (Apis mellifera). SAR QSAR Environ. Res. 2018, 29, 743–754. [Google Scholar] [CrossRef]
  54. Natekin, A.; Knoll, A. Gradient boosting machines, a tutorial. Front. Neurorobot. 2013, 7, 21. [Google Scholar] [CrossRef]
  55. Prešern, J.; Škerl, M.I.S. Parameters influencing queen body mass and their importance as determined by machine learning in honey bees (Apis mellifera carnica). Apidologie 2019, 50, 745–757. [Google Scholar] [CrossRef]
  56. Ramirez-Diaz, J.; Manunza, A.; De Oliveira, T.A.; Bobbo, T.; Silva, V.; Cozzi, P.; Biffani, S.; Stella, A.; Minozzi, G. Using supervised machine learning for honey harvest prediction. In Proceedings of the 12th World Congress on Genetics Applied to Livestock Production (WCGALP) 2022, Rotterdam, The Netherlands, 12–17 July 2022; p. 3365. [Google Scholar] [CrossRef]
  57. Kim, J.; Kim, G. Revealing the Relationship Between Beehives and Global Warming via Machine Learning. In Advances in Information and Communication; Lecture Notes in Networks and Systems; Springer: Berlin/Heidelberg, Germany, 2024; Volume 921. [Google Scholar] [CrossRef]
  58. Ahuja, R.; Chug, A.; Gupta, S.; Ahuja, P.; Kohli, S. Classification and Clustering Algorithms of Machine Learning with their Applications. In Nature-Inspired Computation in Data Mining and Machine Learning; Yang, X.S., He, X.S., Eds.; Studies in Computational Intelligence; Springer: Berlin/Heidelberg, Germany, 2020; Volume 855, pp. 225–248. [Google Scholar] [CrossRef]
  59. Mateo, F.; Tarazona, A.; Mateo, E.M. Comparative study of several machine learning algorithms for classification of unifloral honeys. Foods 2021, 10, 1543. [Google Scholar] [CrossRef] [PubMed]
  60. Boateng, E.Y.; Abaye, D.A. A Review of the Logistic Regression Model with Emphasis on Medical Research. J. Data Anal. Inf. Process. 2019, 7, 190–207. [Google Scholar] [CrossRef]
  61. Mekha, P.; Teeyasuksaet, N.; Sompowloy, T.; Osathanunkul, K. Honey Bee Sound Classification Using Spectrogram Image Features. In Proceedings of the 7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2022, Chiang Rai, Thailand, 26–28 January 2022. [Google Scholar] [CrossRef]
  62. Sherstinsky, A. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Phys. D Nonlinear Phenom. 2020, 404, 132306. [Google Scholar] [CrossRef]
  63. Ruvinga, S.; Hunter, G.J.A.; Duran, O.; Nebel, J.C. Use of LSTM Networks to Identify “Queenlessness” in Honeybee Hives from Audio Signals. In Proceedings of the 17th International Conference on Intelligent Environments, IE 2021, Dubai, United Arab Emirates, 21–24 June 2021. [Google Scholar] [CrossRef]
  64. Braga, A.R.; Freitas, B.M.; Gomes, D.G.; Bezerra, A.D.M.; Cazier, J.A. Forecasting sudden drops of temperature in pre-overwintering honeybee colonies. Biosyst. Eng. 2021, 209, 315–321. [Google Scholar] [CrossRef]
  65. Calders, T.; Verwer, S. Three naive Bayes approaches for discrimination-free classification. Data Min. Knowl. Discov. 2010, 21, 277–292. [Google Scholar] [CrossRef]
  66. da Silva, F.L.; Sella, M.L.G.; Francoy, T.M.; Costa, A.H.R. Evaluating classification and feature selection techniques for honeybee subspecies identification using wing images. Comput. Electron. Agric. 2015, 114, 68–77. [Google Scholar] [CrossRef]
  67. Bouveyron, C.; Girard, S.; Schmid, C. High-Dimensional Discriminant Analysis. Commun. Stat. Theory Methods 2007, 36, 2607–2623. [Google Scholar] [CrossRef]
  68. Pirouz, D.M. An Overview of Partial Least Squares. SSRN Electron. J. 2006. [Google Scholar] [CrossRef]
  69. Se, K.W.; Ghoshal, S.K.; Wahab, R.A. Laser-induced breakdown spectroscopy unified partial least squares regression: An easy and speedy strategy for predicting Ca, Mg and Na content in honey. Measure 2019, 136, 1–10. [Google Scholar] [CrossRef]
  70. Costa, M.C.A.; Morgano, M.A.; Ferreira, M.M.C.; Milani, R.F. Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression. Food Chem. 2019, 273, 85–90. [Google Scholar] [CrossRef]
  71. Sharin, S.N.; Sani, M.S.A.; Jaafar, M.A.; Yuswan, M.H.; Kassim, N.K.; Manaf, Y.N.; Wasoh, H.; Zaki, N.N.M.; Hashim, A.M. Discrimination of Malaysian stingless bee honey from different entomological origins based on physicochemical properties and volatile compound profiles using chemometrics and machine learning. Food Chem. 2021, 346, 128654. [Google Scholar] [CrossRef] [PubMed]
  72. Suarin, N.A.S.; Chia, K.S.; Mohamad Fuzi, S.F.Z. Transfer learning in near infrared spectroscopy for stingless bee honey quality prediction across different months. Knowl.-Based Syst. 2024, 295, 111817. [Google Scholar] [CrossRef]
  73. Zhu, M.; Hastie, T.J.; Walther, G. Constrained ordination analysis with flexible response functions. Ecol. Model. 2005, 187, 524–536. [Google Scholar] [CrossRef]
  74. Huang, D.; Quan, Y.; He, M.; Zhou, B. Comparison of linear discriminant analysis methods for the classification of cancer based on gene expression data. J. Exp. Clin. Cancer Res. 2009, 28, 149. [Google Scholar] [CrossRef] [PubMed]
  75. Jekabsons, G.; Lavendels, J. A comparison of heuristic methods for polynomial regression model induction. Math. Model. Anal. 2008, 13, 17–27. [Google Scholar] [CrossRef]
  76. Thonhual, N. Machine Learning to Examine the Foraging Periods of Bees. In Proceedings of the 2023 18th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), Bangkok, Thailand, 27–29 November 2023; pp. 1–6. [Google Scholar] [CrossRef]
  77. Ao, Y.; Li, H.; Zhu, L.; Ali, S.; Yang, Z. The linear random forest algorithm and its advantages in machine learning assisted logging regression modelling. J. Pet. Sci. Eng. 2019, 174, 776–789. [Google Scholar] [CrossRef]
  78. Calovi, M.; Grozinger, C.M.; Miller, D.A.; Goslee, S.C. Summer weather conditions influence winter survival of honey bees (Apis mellifera) in the northeastern United States. Sci. Rep. 2021, 11, 1553. [Google Scholar] [CrossRef]
  79. Moreira-Filho, J.T.; Braga, R.C.; Lemos, J.M.; Alves, V.M.; Borba, J.V.V.B.; Costa, W.S.; Kleinstreuer, N.; Muratov, E.N.; Andrade, C.H.; Neves, B.J. BeeToxAI: An artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees. AILSCI 2021, 1, 100013. [Google Scholar] [CrossRef]
  80. Noble, W.S. What is a support vector machine? Nat. Biotechnol. 2006, 24, 1565–1567. [Google Scholar] [CrossRef]
  81. Rustam, F.; Sharif, M.Z.; Aljedaani, W.; Lee, E.; Ashraf, I. Bee detection in bee hives using selective features from acoustic data. Multimed. Tools Appl. 2024, 83, 23269–23296. [Google Scholar] [CrossRef]
  82. Cejrowski, T.; Szymański, J.; Mora, H.; Gil, D. Detection of the Bee Queen Presence Using Sound Analysis. In Intelligent Information and Database Systems; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2018; Volume 10752, LNAI. [Google Scholar] [CrossRef]
  83. Williams, S.M.; Aldabashi, N.; Cross, P.; Palego, C. Challenges in Developing a Real-Time Bee-Counting Radar. Sensors 2023, 23, 5250. [Google Scholar] [CrossRef] [PubMed]
  84. Rodrigues, P.J.; Gomes, W.; Pinto, M.A. DeepWings©: Automatic Wing Geometric Morphometrics Classification of Honey Bee (Apis mellifera) Subspecies Using Deep Learning for Detecting Landmarks. Big Data Cogn. Comput. 2022, 6, 70. [Google Scholar] [CrossRef]
  85. Balabin, R.M.; Lomakina, E.I. Support vector machine regression (SVR/LS-SVM)—An alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. Analyst 2011, 136, 1703. [Google Scholar] [CrossRef] [PubMed]
  86. vanEngelsdorp, D.; Traynor, K.S.; Andree, M.; Lichtenberg, E.M.; Chen, Y.; Saegerman, C.; Cox-Foster, D.L. Colony Collapse Disorder (CCD) and bee age impact honey bee pathophysiology. PLoS ONE 2017, 12, e0179535. [Google Scholar] [CrossRef]
  87. Di, N.; Sharif, M.Z.; Hu, Z.; Xue, R.; Yu, B. Applicability of VGGish embedding in bee colony monitoring: Comparison with MFCC in colony sound classification. PeerJ 2023, 11, e14696. [Google Scholar] [CrossRef]
  88. Williams, S.M.; Bariselli, S.; Palego, C.; Holland, R.; Cross, P. A comparison of machine-learning assisted optical and thermal camera systems for beehive activity counting. Smart Agric. Technol. 2022, 2, 100038. [Google Scholar] [CrossRef]
  89. Gomes, P.A.B.; de Carvalho, E.C.; Arruda, H.M.; de Souza, P.; Pessin, G. Exploiting Recurrent Neural Networks in the Forecasting of Bees’ Level of Activity. In Artificial Neural Networks and Machine Learning—ICANN 2017; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2017; Volume 10613, pp. 254–261. [Google Scholar] [CrossRef]
  90. Anwar, O.; Keating, A.; Cardell-Oliver, R.; Datta, A.; Putrino, G. Apis-Prime: A deep learning model to optimize beehive monitoring system for the task of daily weight estimation. Appl. Soft Comput. 2023, 144, 110546. [Google Scholar] [CrossRef]
  91. Kulyukin, V.; Mukherjee, S.; Amlathe, P. Toward Audio Beehive Monitoring: Deep Learning vs. Standard Machine Learning in Classifying Beehive Audio Samples. Appl. Sci. 2018, 8, 1573. [Google Scholar] [CrossRef]
  92. Zgank, A. Bee swarm activity acoustic classification for an iot-based farm service. Sensors 2020, 20, 21. [Google Scholar] [CrossRef]
  93. Berkaya, S.K.; Sora Gunal, E.; Gunal, S. Deep learning-based classification models for beehive monitoring. Ecol. Inform. 2021, 64, 101353. [Google Scholar] [CrossRef]
  94. Alves, T.S.; Pinto, M.A.; Ventura, P.; Neves, C.J.; Biron, D.G.; Junior, A.C.; de Paula Filho, P.L.; Rodrigues, P.J. Automatic detection and classification of honey bee comb cells using deep learning. Comput. Electron. Agric. 2020, 170, 105244. [Google Scholar] [CrossRef]
  95. Makori, D.; Fombong, A.; Abdel-Rahman, E.; Nkoba, K.; Ongus, J.; Irungu, J.; Mosomtai, G.; Makau, S.; Mutanga, O.; Odindi, J.; et al. Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models. ISPRS Int. J. Geo-Inf. 2017, 6, 66. [Google Scholar] [CrossRef]
  96. Giliba, R.A.; Mpinga, I.H.; Ndimuligo, S.A.; Mpanda, M.M. Changing climate patterns risk the spread of Varroa destructor infestation of African honey bees in Tanzania. Ecol. Process. 2020, 9, 48. [Google Scholar] [CrossRef]
  97. Schurischuster, S.; Remeseiro, B.; Radeva, P.; Kampel, M. A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees. In Image Analysis and Recognition; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2018; Volume 10882, pp. 465–473. [Google Scholar] [CrossRef]
  98. Braga, A.R.; Gomes, D.G.; Freitas, B.M.; Cazier, J.A. A cluster-classification method for accurate mining of seasonal honey bee patterns. Ecol. Inform. 2020, 59, 101107. [Google Scholar] [CrossRef]
  99. Batz, P.; Ruttor, A.; Thiel, S.; Wegener, J.; Zautke, F.; Schwekendiek, C.; Bienefeld, K. Semi-automatic detection of honeybee brood hygiene—An example of artificial learning to facilitate ethological studies on social insects. Biol. Methods Protoc. 2022, 7, bpac005. [Google Scholar] [CrossRef]
  100. Wachowicz, A.; Pytlik, J.; Małysiak-Mrozek, B.; Tokarz, K.; Mrozek, D. Edge Computing in IoT–Enabled Honeybee Monitoring for the Detection of Varroa Destructor. Int. J. Appl. Math. Comput. Sci. 2022, 32, 345–413. [Google Scholar] [CrossRef]
  101. Mrozek, D.; Gȯrny, R.; Wachowicz, A.; Małysiak-Mrozek, B. Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning. Appl. Sci. 2021, 11, 11078. [Google Scholar] [CrossRef]
  102. König, A. An in-hive soft sensor based on phase space features for Varroa infestation level estimation and treatment need detection. J. Sens. Sens. Syst. 2022, 11, 29–40. [Google Scholar] [CrossRef]
  103. Kulyukin, V.; Mukherjee, S. On Video Analysis of Omnidirectional Bee Traffic: Counting Bee Motions with Motion Detection and Image Classification. Appl. Sci. 2019, 9, 3743. [Google Scholar] [CrossRef]
  104. Patel, V.; Biggs, E.M.; Pauli, N.; Boruff, B. Using a social-ecological system approach to enhance understanding of structural interconnectivities within the beekeeping industry for sustainable decision making. Ecol. Soc. 2020, 25, art24. [Google Scholar] [CrossRef]
  105. Patel, V.; Boruff, B.; Biggs, E.; Pauli, N. Assessing the influence of variation in forage availability on spatial patterns of beehive migration using a hybrid modelling approach—B-Agent. Appl. Geogr. 2023, 157, 103003. [Google Scholar] [CrossRef]
  106. Majewski, P.; Lampa, P.; Burduk, R.; Reiner, J. Prediction of the remaining time of the foraging activity of honey bees using spatio-temporal correction and periodic model re-fitting. Comput. Electron. Agric. 2023, 205, 107596. [Google Scholar] [CrossRef]
  107. Torresani, M.; Kleijn, D.; de Vries, J.P.R.; Bartholomeus, H.; Chieffallo, L.; Cazzolla Gatti, R.; Moudrý, V.; da Re, D.; Tomelleri, E.; Rocchini, D. A novel approach for surveying flowers as a proxy for bee pollinators using drone images. Ecol. Indic. 2023, 149, 110123. [Google Scholar] [CrossRef]
  108. Liu, Z.; Yin, X.; Li, H.; Qiao, D.; Chen, L. Effects of different floral periods and environmental factors on royal jelly identification by stable isotopes and machine learning analyses during non-migratory beekeeping. Food Res. Int. 2023, 173, 113360. [Google Scholar] [CrossRef] [PubMed]
  109. García, C.A.Y.; Rodrigues, P.J.; Tofilski, A.; Elen, D.; McCormak, G.P.; Oleksa, A.; Henriques, D.; Ilyasov, R.; Kartashev, A.; Bargain, C.; et al. Using the Software DeepWings© to Classify Honey Bees across Europe through Wing Geometric Morphometrics. Insects 2022, 13, 1132. [Google Scholar] [CrossRef] [PubMed]
  110. Dimitrios, K.I.; Bellos, C.V.; Stefanou, K.A.; Stergios, G.S.; Andrikos, I.; Katsantas, T.; Kontogiannis, S. Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound. Signals 2022, 3, 807–822. [Google Scholar] [CrossRef]
  111. Salem, Z.; Radspieler, G.; Griparić, K.; Schmickl, T. Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms. In Machine Learning, Optimization, and Big Data; Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 309–321. [Google Scholar] [CrossRef]
Figure 1. An ML-based predictive model of an AI system, with its general structure, showing the training and testing phases (1. training database; 2. ML algorithm; 3. predictive model; 4. monitoring tools; 5. test dataset; 6. practical/developmental usage of outcome).
Figure 1. An ML-based predictive model of an AI system, with its general structure, showing the training and testing phases (1. training database; 2. ML algorithm; 3. predictive model; 4. monitoring tools; 5. test dataset; 6. practical/developmental usage of outcome).
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Table 1. Summary of the machine learning algorithms used in the beekeeping studies used in this review.
Table 1. Summary of the machine learning algorithms used in the beekeeping studies used in this review.
ModelShort DescriptionUsage 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

AMA Style

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 Style

Astuti, 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

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