Developing cost-effective and environmentally sustainable pest strategies: integrating biological control, precision agriculture and AI-driven monitoring systems.

Effective pest management is one of the greatest challenges facing agriculture today, with potential solutions considering the costs of implementation and environmental impact. As traditional methods involving chemicals such as pesticides lead to biodiversity loss, soil erosion, and even resistance against these chemicals, there is a growing need for sustaining approaches. This research considers integrating biological control with precision agriculture and artificial intelligence monitoring systems to enhance the effectiveness and sustainability of pest management efforts. Maintaining ecological balance and biological control utilizes natural predators and parasitoids of pests for controlled pest eradication. AI-powered monitoring systems assist in predicting and detecting pests using machine learning algorithms, allowing for faster responses to minimize the usage of chemical pesticides. By using these innovative approaches, this research hopes to encourage sustainable pest control practices, leading to better crop production, lower input expenses, and sustainable agricultural practices in the long run. The result showed how AI technology can aid in pest surveillance control, evaluate the effect on ecological systems, and assess productivity and improvement from precision agriculture. This new information advances the literature on sustainable pest management by offering a greater understanding of adaptable and scalable solutions for various agroecosystems.