Artificial Intelligence in Environmental Science: Innovations and Challenges

Intelligence (AI) possesses substantial transformative capabilities in tackling paramount environmental challenges, encompassing climate change, biodiversity degradation, pollution management, and sustainable resource utilisation of plants. This manuscript comprehensively examines AI applications within these spheres, accentuating progressions in monitoring, modelling, and decision-making processes. Ultimately, we delineate prospective avenues to augment AI's responsible and productive integration in environmental science. Artificial Intelligence (AI) is fundamentally altering the landscape of environmental science by facilitating groundbreaking solutions to urgent ecological dilemmas such as climate change, plant-microbe interactions, biodiversity preservation, and Plant-based drug discovery. This review investigates the transformative applications of AI, featuring sophisticated climate change adaptation, weed identification and management, habitat delineation, and plant conservation aspects. Machine learning and deep learning techniques have demonstrated their critical role in analysing extensive datasets, forecasting environmental patterns, and guiding data-driven decision-making processes. To mitigate these challenges, future research endeavours must prioritise the development of interpretable AI models, enhancing algorithmic energy efficiency, and promoting interdisciplinary collaborations. By harmonising technological progress with sustainability objectives, AI harbours the potential to significantly bolster global environmental resilience and plant conservation initiatives.