## 5 Machine Learning and Pollination  
Machine learning (ML) can play a significant role in the context of pollination, offering innovative solutions and insight to address challenges and optimize pollination processes. Below are several ways in which machine learning is applied to pollination:  
1. Predictive modeling: ML algorithms can analyze historical data on pollination success, environmental conditions, crop yields, and phenology to create predictive models. These models can help to predict optimal times for pollination, considering factors such as weather patterns, bloom asynchrony, and the availability of pollininators [52; 53; 54].
2. Automated monitoring: ML-powered monitoring systems can analyze images or sensor data to track the health and development of crops. This real-time monitoring allows for the early detection of issues related to pollination, such as low pollination rates or the presence of pests that could affect pollinators [55; 56].
3. Optimizing pollination strategies: Machine learning algorithms can optimize artificial pollination strategies based on a variety of factors [57; 58]. These include the type of crop, environmental conditions, and the efficiency of different pollination methods [59; 60]. This can lead to more targeted and effective pollination efforts [61].
4. Identification of pollinator behavior: ML can be used to analyze the behavior of natural pollinators, such as bees, by processing video footage or sensor data. Understanding pollinator behavior can provide insight into their preferences, movement patterns, and efficiency in pollinating specific crops [62; 63].
5. Genetic analysis: Machine learning techniques can analyze genetic data related to plant characteristics, including traits associated with pollination [64; 65]. This information can contribute to breeding programs aimed at developing crops that are more resilient to environmental challenges and more compatible with artificial pollination methods [66; 67].
6. Decision support systems: ML-based decision support systems can assist farmers in making informed choices related to pollination strategies. These systems can consider a range of variables and provide recommendations for optimal pollination practices [68; 69].  
Data integration: Machine learning excels at integrating and analyzing large datasets from various sources. In the context of pollination, this can include data on weather conditions, soil quality, plant health, and more. The integrated data can provide a comprehensive understanding of the factors influencing pollination success [70; 71; 72].  
While machine learning holds great promise for improving pollination processes, it is essential to recognize the importance of interdisciplinary collaboration between machine learning and pollination, as presented by the VOS viewer analysis in Figure 5. Based on Figure 5, up to date knowledge as of 2022 (Figure 5 items with yellow dots) requires: \"anatomy and histology\", \"classification of information\", \"classification algorithms\", \"image classification\", \"neural networks\", \"population statistics\", \"robotics\", \"machine learning\", \"deep learning\", and \"meta-heuristic algorithms\" (Figure 5, items with yellow dots).  
Even more, using VOS viewer mapping software and the Scopus bibliographic database based on the search strategy criteria \"Aerial and Pollination\" (Figure 6) revealed that all of the abovementioned criteria or technologies show promise, but they still require important improvements, such as \"antennas\", \"aircraft detection\", \"aircraft control\", \"MATLAB\", \"internet of things\", \"population diversity\", \"flower population algorithm\", \"swarm intelligence algorithm\", \"learning algorithm\", \"learning systems\", \"deep learning\", \"quadrotor control\", \"image enhancement\", \"auxiliary pollination\", \"supplementary pollination\", and \"convolutional neural networks\" (Figure 6, items with yellow dots).  
Figure 5: Co-keyword network visualization based on “Machine Learning” and “Pollination”.