Bt Research 2024, Vol.15, No.5, 240-247 http://microbescipublisher.com/index.php/bt 245 accuracy of AI predictions. In the context of resistance monitoring, the precision and accuracy of data collection methods, such as visual estimates of maize injury, can vary widely among observers, leading to inconsistent results (Dorman et al., 2021). 6.2 Computational complexity The computational complexity of AI models is another significant challenge. Advanced AI algorithms, such as those used in predicting the structural intermediates of Cry toxins, require substantial computational resources. For example, the use of Alphafold-2 to predict the oligomeric structures of Cry1Aa and Cry4Ba toxins involves complex calculations that demand high computational power (Torres et al., 2023). This complexity can be a barrier to the widespread adoption of AI tools in Bt toxin research, especially in resource-limited settings. 6.3 Biological complexity and variability The biological complexity and variability of Bt toxin-insect interactions pose additional challenges for AI applications. The interactions between Bt toxins and insect pests are influenced by numerous factors, including genetic variation, environmental conditions, and nutritional status. For instance, the nutritional status of insect herbivores can significantly affect their susceptibility to Bt toxins, with dietary protein and carbohydrates mediating plasticity in susceptibility (Deans et al., 2016). Moreover, the phenomenon of Bt-induced hormesis, where low doses of Bt toxins can stimulate biological processes in resistant insect populations, adds another layer of complexity to the prediction models (Campos et al., 2019). These biological variabilities must be accurately captured and incorporated into AI models to ensure reliable predictions. 7 Concluding Remarks The integration of artificial intelligence (AI) in predicting Bt toxin-insect interactions has shown significant promise in enhancing pest control strategies. Bt crops, which produce insecticidal toxins from Bacillus thuringiensis, have been effective in managing pest populations and reducing the use of conventional insecticides. However, the evolution of pest resistance to Bt toxins remains a critical challenge. AI techniques, including machine learning algorithms and sensing technologies, have been employed to improve the detection, diagnosis, and management of pest resistance. These advancements have the potential to optimize integrated pest management (IPM) programs by providing predictive and prescriptive insights into pest behavior and resistance patterns. Future research should focus on several key areas to further enhance the integration of AI in pest control. Continued efforts are needed to discover and engineer new Bt toxins that can overcome existing resistance mechanisms in pests. Tools like CryProcessor can facilitate the mining of novel Cry toxins from sequencing data. In-depth studies on the genetic and molecular mechanisms of Bt resistance, such as the role of ABC transporters and other receptor proteins, are essential for developing effective counter-resistance strategies. Implementing advanced AI models to predict the emergence and spread of resistance can help in devising timely and targeted interventions. These models should incorporate environmental, genetic, and behavioral data to enhance their accuracy. Research should aim to integrate AI tools with traditional IPM practices, focusing on the synergistic use of Bt toxins, plant allelochemicals, and conventional insecticides to manage pest populations effectively. The integration of AI in pest control offers several insights and benefits. AI techniques, such as image segmentation and feature extraction, can significantly improve the monitoring and early detection of pest infestations, allowing for more precise and timely interventions. AI can aid in understanding and predicting resistance patterns, enabling the development of more robust and sustainable pest management strategies. For instance, AI-driven analysis of genetic data can identify key resistance genes and their mutations, facilitating targeted countermeasures. By combining AI with traditional IPM practices, it is possible to create more effective and environmentally friendly pest control programs. AI can help in optimizing the use of Bt crops, refuges, and other control measures to delay resistance and maintain the efficacy of Bt toxins. The use of AI in pest control aligns with the goals of sustainable agriculture by reducing reliance on chemical insecticides, minimizing environmental impact, and promoting ecological balance.
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