Predictive Algorithms for Smart Agriculture

Recent innovations in agriculture have made it smarter, more intelligent, and précised. Due to the technological advancement paradigm shift of agriculture practices from traditional to wireless digital incorporation of IoT, AI/ML, and Sensor technologies. Machine learning is a critical technique in agriculture for ensuring food assurance and sustainability. The machine learning algorithm starts from scratch to the final step—The selection of Crop, Soil Preparation, Seed Selection, Seed sowing, Irrigation, Fertilizer/Manure Selection, Control of Pests/weeds/diseases, Crop Harvesting, and Crop distribution for sales. ML algorithm suggests the right step for high-yield crops and precision farming. This article discusses how predictive ML supervised classification algorithms—especially K-Nearest Neighbor (KNN) can be helpful in the selection of crops, fertilizer to be used, corrective measures for the precision yield, and irrigation needs by looking at different parameters like climatic conditions, soil type, and previous crops grown in the field. The accuracy of algorithms comes out to be more than 90% depending on some uncertainties in the collection of data from different sensors. This results in well-designed irrigation plans based on the specific field conditions and crop needs.