Intelligent Fertilizer Recommendations to Improve Crop Yield Using Wireless Sensor Network

For the expanding populace, farming is crucial in supplying food and other resources. However, the pressure on farmers to maximize crop yields is rising along with the demand for food production. Giving the crops the appropriate amount of nutrients at the appropriate time can help to achieve the goal. Artificial intelligence (AI) and wireless sensor networks (WSN) are two powerful technologies which can help farmers to achieve this goal to increase crop yield. In this article, a stacking method utilizing machine learning (ML) and artificial intelligence (AI) is suggested to create fertilizer recommendations for agricultural operations. The aim of this study is to develop an intelligent system that utilizes data from senso and algorithms based on machine learning to optimize fertilizer application and boost crop yields. The study includes the use of the Ensemble learning technique to develop a fertilizer recommendation. It takes the prediction of base models SVM, Decision tree, and KNN as new features, then train the meta-model which is Random Forest classifier and makes predictions using the train meta-model with optimal performance.