Soil is a heterogeneous and complex natural resource that is the factual basis of almost all agriculture production activities. The soil’s inherent nutrients or physiochemical properties help the researchers better understand the soil ecosystem dynamics and play a crucial role in guiding farmland decision-makers in their routine decisions. Therefore, the accurate forecasting of soil leads to improved and better soil health management (SHM). The recent advances in sensing and computational technologies have led to the expanding accessibility of farmland data either obtained distantly or proximally. The increasing availability of massive data and unreservedly accessible open-source algorithms have prompted a quickened use of machine learning (ML) procedures to investigate soil conditions. Therefore, to understand the usage of ML techniques in exploring soil properties and related applications, this paper concentrates on reviewing and analyzing ML techniques precisely to predict and assess soil properties for improved decisions on agricultural SHM. The article also explores various other vital factors like algorithms, implementation tools, and performance metrics employed in numerous soil assessment application domains and different challenges and future research directions for SHM using ML techniques. The detailed assessment concludes that the response for ML in the prediction and evaluation of soil properties for SHM is very promising for the sustainable growth of agriculture.

Soil performs a significant role in the agricultural ecosystem by supplying essential nutrients and a conducive environment for plants’ growth and crop yield. Inside the agribusiness space, the soil classification is a crucial work that gives good classification results for different soil types. The taxonomy provides an excellent rating for inherent soil elements. This work investigates the accuracy of three well-known classification models like K-Nearest Neighbor (k-NN), Naive Bayes (NB) and, Decision Tree (DT) using a publically available agricultural soil dataset. Post investigation, an Ensemble Classifier (EC) is proposed by fusing the above mentioned three classifiers. The experimental results indicate that EC has the highest accuracy of 84% in comparison to the NB (72.90%), k-NN (73.56%), and DT (80.84%). So it performs better than the other classifiers. The results infer that EC would be useful for accurate classification of soil types in the agricultural domain.

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