Paper Details

The rapid advancement of Artificial Intelligence (AI) in the verdant sphere of plant sciences has been gaining significant traction, and to understand this momentum is undoubtedly imperative. Hence, this paper focuses on highlighting the applications and limitations of AI in the various disciplines of plant sciences. As established in the recent decade, AI can automate plant identification, facilitate comparative studies, analyse results from various tests including genotypical, anatomical and pathological, reform precision agriculture and horticulture, predict outputs and costs, amongst a host of other operatives. In the field of agriculture, AI holds great potential particularly in scrutinizing farm fields for disease identification- pathological and/or physiological, early disease detection, aiding with plant nutrition, and creating superior crop varieties. AI has also proven to be a valuable addition to modern plant propagation techniques by making them more efficient and sustainable. Although current AI models face challenges, such as identifying new species, maintaining heterogeneity in factual and ground data sets, standardization of data and related softwares, providing a definite linkage between predictive analytics and field research, they can be mitigated through machine learning techniques, extensive R&D and collaborative approaches. To reinstitute, AI is rapidly changing the current landscape of the domain in question and stands to revolutionise various aspects of it, but our aim has to be to routinely and ethically integrate it in the fabric of plant sciences in order to open new avenues while focusing on the long-term goal of achieving sustainability and food security.

Artificial Intelligence, AI, Plant Sciences, Machine Learning