Crop Yield Prediction: A Fusion of IoT and Machine Learning for Precision Agriculture

The countries of the Indian subcontinent are indeed very reliant on agriculture for their daily necessities. Among that country, the majority of agricultural production in Bangladesh is categorized as traditional subsistence farming. Rice, wheat, corn, legumes, fruits, vegetables, meat, fish, seafood, and dairy products are among the agricultural products manufactured in Bangladesh. The main staple food of Bangladesh is rice. The scarcity of arable land and natural resources emphasizes the significance of creating innovative agricultural technologies to improve productivity and satisfy future demand. Precision agriculture is a difficult task to execute. In this study, we used meteorological data obtained from the Bangladesh Bureau of Statistics (BBS) and Bangladesh Meteorological Department from more than 8 districts in Bangladesh over 45 years. The primary objective of this study is to create a one-of-a-kind machine learning model utilizing seven environmental factors. Our model also improves forecast accuracy of the best qualities for overcoming hunger challenges. Based on our collected Aus rice dataset, we utilized voting regression (VR) with a novel combination of MLA. The VR's concept is to integrate MLA and return the average anticipated values. Compared with other MLA, our proposed algorithm achieved the highest R ² of 0.8928 in a study on Aus rice yield prediction. In addition, we proposed a self-designed IoT device to automatically gather data from agricultural fields and thereby increase crop production forecasts by incorporating the data into our proposed model. The proposed system will indeed be immensely helpful to a country's agro-economic advancement.