Smart Crop Selection: Harnessing Machine Learning for Sustainable Agriculture in the Era of Industry 5.0

Presently, farmers face significant challenges in achieving optimal crop yields, largely due to issues with soil fertility and appropriate crop selection exacerbated by shifting climate patterns. Traditional farming knowledge often falls short in adapting to these changes, leading to repetitive crop cycles that worsen soil fertility. The present works focus on addressing the shortcomings by proposing a precise and useful solution using machine learning (ML) algorithms to identify crops for maximal yield. In this context, the introduction of a machine learning model named “Smart Crop Selection (SCS)” is a significant contribution. SCS utilizes data on meteorological and soil factors, to predict the most suitable crops for a given environment. This approach offers enhanced reliability over traditional manual testing methods, which are prone to human errors and may not consider the complexity of shifting climate patterns. Incorporating this technology is in line with the principles of Industry 5.0, which signifies the symbiotic relationship between humans and cutting-edge technologies in manufacturing and other sectors. In the agricultural sector, Industry 5.0 promotes the utilization of intelligent systems like SCS will equip farmers with valuable data insights, helping them to make informed decisions on which crops to grow and the best practices for cultivating them. The use of ML algorithms and advanced data analysis has strong potential to revolutionize the agriculture and its industry.