Study on Machine Learning and Back Propagation for Crop Recommendation System

India may be the largest exporter of farm products, but the country’s farms are not very productive. Inadequate agricultural production means farmers bring home much less money. The producers need a performance improvement if there is a rise in their revenue. Farmers may boost output by cultivating land more effectively by first determining what will thrive there. The land’s potential production can be enhanced by growing the correct crops. As a result, crop recommendation algorithms may be quite useful for agriculturalists. Crop yields are influenced by a wide variety of external influences. The production is affected by environmental conditions like as heat, moisture, soil pH, precipitation, and the availability of nutrients like potash, ammonium, & phosphate. There is a lot of confusion among farmers regarding which crops should be produced in particular regions to get the highest possible yield and financial return. As a result, the purpose of this study is to show how a ml algo may be used to forecast crops and associated prices. Soil is fundamental to the proper functioning of the terrestrial ecosystem, and its primary duty is the cultivation of crops for the purpose of providing food for the world’s ever-increasing population. Yet global soil deterioration poses a serious challenge to food security. The stresses of urbanization and industry, among others, contribute to soil degradation. Habitat loss, altered green spaces, soil erosion, unchecked livestock, illegal dumping, and ineffective land management are all major contributors to soil deterioration. More than 1.5 billion people are in danger of losing their way of life due to the rapid pace of land degradation, which now stands at 24 percent (350M sq.km). In this situation, keeping up with the rising demand for food is a problem made more difficult by the need to maintain soil quality). As a result, maintaining agricultural output and adapting to the uncertainties brought by climate change needs constant evaluation and monitoring of SQ. However, assessing soil quality is challenging owing to subjectivity and complexity because of the wider range of soil uses. Soil quality may be measured using an index that allows for chronological & geographical comparison between various land use & control strategies.