Soil Nutrients Prediction and Optimal Fertilizer Recommendation for Sustainable Cultivation of Groundnut Crop using Enhanced-1DCNN DLM

Abstract

Cultivation of crops and their parallel production yields hugely depend upon the fertility composition of the soil in which the crops are being cultivated. The prime fertility factors which contribute towards the health of the soil are the available soil nutrients. Varying climatic conditions and improper cultivation patterns have resulted in unpredictable growth and yield of the groundnut crops, one of the major cause for the fluctuation seen in groundnut pod growth patterns and production, is the differing soil nutrient compositions of the land which is under cultivation. The unnecessary usage of excessive artificial fertilizers to boost the soil strength, without properly diagnosing the exact nutritional need of the soil required for the conducive growth of the crop has led to the imbalanced distribution of the soil's major macro-nutrients constituents such as (Phosphorous (P), Potassium (K) and Nitrogen (N)). In this research article, we have made a detailed investigation for nutrient prediction mechanism of the soil nutrient datasets taken under investigation of a specific geographic location from one of the major groundnut cultivating districts (Villupuram) in the state of Tamil Nadu and have proposed a Soil nutrients prediction scheme and optimal fertilizer recommendation model for sustainable cultivation of groundnut crop using Enhanced-1DCNN DLM. This Investigation model utilizes the natural compact robust features of 1DCNN in classifying the major macro nutrients(N,P,K)on the basis of low, Medium and high values. Based on the generated heatmap results the correlation between certain macronutrients and their corresponding micronutrient presence is classified. This proposed model has been compared for its performance and error measures with existing SVM, Naïve Bayes and ANN models and has proved to be outperforming all the compared baseline models by preserving the original data distribution with an overall accuracy of 99.78%.