Agricultural Farm Production Model for Smart Crop Yield Recommendations Using Machine Learning Techniques †

Abstract

Smart agricultural monitoring is the use of cutting-edge technology to manage all elements impacting plants and lowering crop yield quality. The main objective of smart crop monitoring and management is to guarantee farmers optimal productivity. Additionally, the market for worldwide smart crop management is expanding continuously as a result of the rising need for smart agricultural techniques. Machine learning techniques have the potential to be utilized to provide intelligent agricultural yield suggestions that will assist farmers in increasing their crop yields and profitability. Machine learning algorithms are used to analyze massive collections containing previous yield statistics, meteorological data, soil data, and other parameters in order to discover patterns and associations that might be used to predict agricultural yields. The methodology used in this system is that the farmer must enter the details of conditions in the field. Once entered into the system, the data are analyzed. This predicts the state of environmental conditions and predicts the crop that is suitable under these situations to give a greater yield. A web application is also built here for the farmer to analyze the information regarding their crops and to generate relevant reports. To find better crops under various conditions, the k-nearest neighbor (KNN) technique is used. Finally, the farmer achieves better results based on the conditions in the field, enabling them to plant the crop that is appropriate to those conditions. The proposed system helps a huge number of farmers by using IoT (Internet of Things) devices and web applications for smart irrigation.