Machine Learning Analysis of Pesticide Effects on Agricultural Pollinator Diversity

Abstract

Pollinators are a crucial component of agroecosystems that greatly affect crop production, plant reproduction, ecological stability, and biodiversity conservation. On the other hand, increased agricultural intensification along with extensive pesticide application is posing a significant threat to the pollinator population through its effect on pollinator survival, behavior, reproductive efficiency, and distribution. Traditional approaches to evaluate the impact of pesticides depend upon field studies and laboratory toxicity tests that often come with some restrictions in understanding the complex interaction between chemical exposure, environmental conditions, and biological responses. The current study designs a machine learning approach to evaluate the effects of pesticide exposure on agricultural pollinator diversity based on the parameters related to pesticides, environment, agriculture, and biology. Several machine learning models like Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting were analyzed in order to detect patterns corresponding to pollinator population shifts and predict changes in diversity. As a result of the analysis, it became clear that higher exposure to pesticides resulted in lower pollinator abundance and diversity, while favorable habitat conditions and environmental factors contributed to better distribution of pollinators. In comparison to the other models, the developed machine learning model provided the highest accuracy of predictions at 95.2%, showing its efficiency in detecting complex ecological connections and providing assistance in biodiversity analysis. These results show that ecological analysis performed with the help of machine learning models provides an opportunity for identification of the pesticide-related risks and improvement of pollinator monitoring and sustainable management of agricultural environment. The developed machine learning model provides a way for data-driven approach to conservation efforts and enables to detect potential threats to pollinator communities in advance. Potential avenues for future research include the integration of ecological monitoring that is updated in real time, large environmental data sets, and learning algorithms.