This academic source proposes integrating artificial intelligence and molecular tools to transform pest management toward resilient and sustainable agriculture. It argues that deep learning models, such as neural networks, can identify insects with high accuracy, overcoming the slowness of manual monitoring and reducing reliance on chemical insecticides. Through predictive analyses and sensor-based technologies, these approaches enable proactive interventions that optimize resource use and protect local biodiversity. However, the authors emphasize that the success of such systems depends on the development of diverse datasets and ethical collaboration that includes small-scale farmers. Ultimately, the article presents digital technologies as a key pillar for ensuring global food security in the face of ongoing environmental degradation.
