Crop Yield Maximization Using an IoT-Based Smart Decision. (17th May 2022)

• Record Type: Journal Article
• Authors: Ikram, Amna; Aslam, Waqar; Aziz, Roza Hikmat Hama; Noor, Fazal; Mallah, Ghulam Ali; Ikram, Sunnia; Ahmad, Muhammad Saeed; Abdullah, Ako Muhammad; Ullah, Insaf
• Other Names: Khan Waliullah Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-05-17
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/2022923
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Today, farmers are suffering from the low yield of crops. Though right crop selection is the main boosting key to maximize crop yield by doing soil analysis and considering metrological factors, the lack of knowledge about soil fertility and crop selection is the main reason for low crop production. In the changed current climate, the farmers having primitive knowledge about conventional farming are facing challenges about making sagacious decisions on crop selection. The selection of the same crop in every seasonal cycle makes the low soil fertility. This study is aimed at making an efficient and accurate system using IoT devices and machine learning (ML) algorithms that can correctly select a crop for maximal yield. Such a system is reliable as compared to the old laboratory testing manual systems, which bear the chances of human errors. Correct selection of a crop is predominantly a priority in agricultural arena. As a contribution, we propose an ML-based model, Smart Crop Selection (SCS), which is based on data of metrological and soil factors. These factors include nitrogen, phosphorus, potassium, CO2, pH, EC, temperature, humidity of soil, and rainfall. Existing IoT-based systems are not efficient as compared to our proposed model due to limited consideration of these factors. In the proposed model, real-time sensory data is sent to Firebase cloud for analysis. Its results are also visualized on the Android app. SCS ensembles the following five ML algorithms to increase performance and accuracy: Decision tree, SVM, KNN, Random Forest, and Gaussian Naïve Bayes. For rainfall prediction, a dataset containing historical data of the last fifteen years is acquired from Bahawalpur Agricultural Department. This dataset and an ML algorithm, Multiple Linear Regression leverages prediction of the rainfall in future, a much-desired information for the health of any crop. The Root Mean Square Error of the rain fall prediction model is 0.3%, which is quite promising. The SCS model is trained for 11 crops' prediction, while its accuracy is 97% to 98%.