10th International Congress on Information and Communication Technology

Authors - Md Fahim Afridi Ani, Abdullah Al Hasib, Munima Haque
Abstract - This research explores the possibility of improving insect farming by integrating Artificial Intelligence (AI) unlocking the complicated relationship between butterflies and plants they pollinate to reconsider the way species are classified and helping to redraw farming practices for the butterflies. Traditional methods of butterfly classification are morphologically and behaviorally intensive, thus mostly very time-consuming to conduct considering that most of them have a high level of subjective interpretation. We therefore apply our approach to ecological interactions involving butterfly species and their respective plants for efficient data-driven solutions. This also focuses on the application of AI in making full benefits from butterfly farming, trying to determine where each species will be best located. The system will, therefore, classify and manage butterflies with much more ease, saving time and energy usually used in conventional classification methods hence on to the farmer or industrial client. The research deepens the understanding of insect-plant relationships for better forecasting of butterfly behavior and, therefore, healthier ecosystems through optimized pollination and habitat balance. For that purpose, a dataset of butterfly species and related plants was developed, on which machine learning models were applied, including decision trees, random forests, and neural networks. It tuned out that the neural network outperformed the others with an accuracy of 93%. Apart from classification, it helps in the identification of a habitat to provide the best conditions possible for the rearing of butterflies. Application of AI in this field simplifies the work of butterfly farming hence being an important tool to be used in improving growth and conservation of biodiversity. Integrating machine learning into ecological research and industry provides scalable, time-efficient solutions for the classification of species toward the sustainable farming of butterflies.