Integrated artificial intelligence in pest and disease management

Abstract

The world is also losing up to 40 percent of the agricultural produce to intractable farm pests and diseases and they are necessitating inevitable intervention and mitigation strategies that look innovative. Conventional methods- such as bare treatments by means of chemicals and manual measurements, are generally inadequate due to dynamic resistance, dearth of labor and so on. The development of the pest and disease control with artificial intelligence (AI) has disrupted the entire process due to amplified surveillance, prediction, and precision control ensured with the help of data-driven methodologies. The components of AI-built systems are machine learning, computer vision and remote sensing applied to predict pests earlier and identify the species and better foresee the outbreak. Such technology may support Integrated Pest Management (IPM) to provide actionable decision-support, provide intervention timing to maximize the benefit of intervention, and reduce agrochemical usage. UAVs armed with these facilitate real-time surveillance, IoT-enabled monitoring and mobile diagnostic programs, intelligent prescription of solutions to farmers, large and small, are enable-weighted. However, data heterogeneity, rural connectivity gaps and model behavior in other ecological contexts present problems to the implementation of large scale. The fact that studies continue to be interdisciplinary makes it evident that there is a need to ensure that AI is strongly incorporated in various aspects of agriculture not only to ensure food security but also environmental sustainability. The review is a compilation of prevailing events, technological deliverables and impediments to execution with the critically-reviewing role of AI in pest and disease remain and its cohesion with environmentally friendly methods of managing crop protection.