Soil Nutrient Analysis and Yield Prediction With Neuro‐ <scp>ML</scp> Ensemble Model Using <scp>IoT</scp> ‐ <scp>WSN</scp> Approach: In Context to India's Agricultural Sector

ABSTRACT Agriculture is a backbone of the Indian economy and people's lives. In agriculture land, soil is the most important element on which the quality of production and efficiency depends to the maximum extent. Phosphorus (P), Nitrogen (N), Potassium (K), and the potential of hydrogen (pH) are the key nutrients in soil. An efficient crop recommender and prediction system is needed to optimize agriculture practices considering the escalating demand for more food. Traditional time‐consuming and manual farming should be replaced with a smart agriculture framework using the integration of technologies like the Internet of Things (IoT), Wireless Sensor Network (WSN), and Machine Learning (ML). This paper proposed an IoT‐WSN driven crop management system with Neuro‐ML Ensemble Model, utilizing LoRaWAN Gateway, that can be deployed in the agriculture field to collect real‐time soil parameters. In this paper for soil nutrient analysis, the author used various ML algorithms such as Naive Bayes (NB), Logistic Regression (LR), K‐Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Ada Boost (AB), Gradient Boosting (GB), and Support Vector Machine (SVM) and recommending a suitable ML algorithm for the crop recommender system. For crop yield prediction, the author has developed and recommended a customized GB Algorithm with an accuracy of 98.80%, and for the fertilizer recommendation system, the author has suggested CNN‐BiGRU which outperforms other approaches like BiGRU and CNN with an average accuracy rate of 92.48%. The author presented work with respect to the Indian agriculture sector and compared ML algorithms with state‐of‐the‐art datasets available on some government websites of India, and used by other authors, with a dataset collected by the author from hardware using Raspberry Pi. For crop recommendation and forecasting, the Neuro‐ML Ensemble model employs the Neuro‐ML, which combines neural networks (NN) with the ML models. This research aspires to assist farmers in opting for suitable crops as per their environmental suitability and situation by analyzing and predicting which crops suit well to fit the parameters required to enhance crop growth like soil nutrients, soil moisture, soil pH, and rainfall, etc. The author obtained accuracy for various ML models used in the framework. For NB, LR, KNN, SVM, DT, and RF, the author obtained accuracies of 99.54%, 96.36%, 95.90%, 96.81%, 98.86%, and 99.31%, respectively, using the Kaggle dataset available as open access. Through a dataset collected by the authors, we obtained accuracies of 94.54%, 91.36%, 92.72%, 92.73%, 86.36%, and 94.54% for NB, LR, KNN, SVM, DT, and RF, respectively. The author found that Naive Bayes (NB) outperforms the other machine learning algorithms, such as KNN, SVM, LR, Decision Tree, RF, and AB, and is the best algorithm suited for crop yield.