The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation.

The innovation in science and technical knowledge has prompted an enormous amount of information for the agrarian sector. Machine learning has risen with massive processing techniques to perceive new contingencies in agricultural development. Machine learning is a novel onset for the investigation and determination of unpredictable agrarian issues. Machine learning models actualize the need for scaling the learning model’s performance. Feature selection can impact a machine learning model’s performance by defining a significant feature subset for increasing the performance and identifying the variability. This paper explains a novel hybrid feature extraction procedure, which is an aggregation of the correlation-based filter (CFS) and random forest recursive feature elimination (RFRFE) wrapper framework. The proposed feature extraction approach aims to identify an optimal subclass of features from a collection of climate, soil, and groundwater characteristics for constructing a crop-yield forecasting machine learning model with better performance and accuracy. The model’s precision and effectiveness are estimated (i) with all the features in the dataset, (ii) with essential features obtained using the learning algorithm’s inbuilt ‘feature_importances’ method, and (iii) with the significant features obtained through the proposed hybrid feature extraction technique. The validation of the hybrid CFS and RFRFE feature extraction approach in terms of evaluation metrics, predictive accuracies, and diagnostic plot performance analysis in comparison with random forest, decision tree, and gradient boosting machine learning algorithms are found to be profoundly satisfying.