Machine Learning- and Feature Selection-Enabled Framework for Accurate Crop Yield Prediction - Sandeep Gupta; Angelina Geetha; K. Sakthidasan Sankaran; Abu Sarwar Zamani; Mahyudin Ritonga; Roop Raj; Samrat Ray; Hussien Sobahi Mohammed (Journal of Food Quality)
Agriculture is crucial for the existence of humankind. Agriculture provides a significant portion of the income for many people all around the world. Additionally, it provides a large number of work possibilities for the general public. Numerous farmers desire for a return to the old-fashioned techniques of farming, which provides little profit in today’s market. Long-term economic growth and prosperity are dependent on the success of agriculture and associated companies in the United States. Agribusiness crop yields may be increased by carefully selecting the right crops and putting in place supportive infrastructure. Weather, soil fertility, water availability, water quality, crop pricing, and other factors are taken into consideration while making agricultural predictions. Machine learning is critical in crop production prediction because it can anticipate crop output based on factors such as location, meteorological conditions, and season. It is advantageous for policymakers and farmers alike to be able to precisely estimate crop yields throughout the growing season since it allows them to anticipate market prices, plan import and export operations, and limit the social cost of crop losses. The use of this tool assists farmers in making informed decisions about which crops to grow on their land. In this study, a machine learning framework for agricultural yield prediction is presented. Crop information is collected in an experiment’s data set. Then, feature selection is performed using the Relief algorithm. Features are extracted using the linear discriminant analysis algorithm. Machine learning predictors, namely, particle swarm optimization-support vector machine (PSO-SVM), K-nearest neighbor, and random forest, are used for classification.