Hybrid Whale Archimedes Optimization–based MLPNN model for soil nutrient classification and pH prediction

• Published in: Environmental science and pollution research international 2023-10, Vol.30 (50), p.109389-109409
• Main Authors: Raman, Prabavathi, Chelliah, Balika Joseph
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural industry; Agricultural land; Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Boron; Classification; Comparative studies; comparative study; cropland; data collection; Earth and Environmental Science; Economics; Ecotoxicology; Environment; Environmental Chemistry; Environmental factors; Environmental Health; Environmental quality; Fertilizers; hybrids; hydrogen; Multilayer perceptrons; Neural networks; Nutrients; Optimization; Organic carbon; pH effects; phosphorus; potassium; prediction; Predictions; Profitability; Research Article; soil; Soil classification; Soil fertility; Soil improvement; Soil nutrients; soil quality; Soils; Villages; Waste Water Technology; Water Management; Water Pollution Control; whales
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-023-29498-2

Soil fertility and environmental factors play an important role in improving productivity and cropland quality in the agricultural sector. A new prediction and classification model for the potential of soil nutrients and hydrogen (pH) levels is proposed. The proposed model, Hybrid Whale Archimedes Optimization–based Multilayer Perceptron Neural Network (HWAO-MLPNN), is utilized for soil features classification of pH levels, and the soils are collected from the villages such as phosphorous (P), organic carbon (OC), boron (B), and potassium (K). The village-wise soil fertility prediction and classification model aims to improve soil health, reduce harmful fertilizer usage, enhance environmental quality, and achieve more profits. The proposed model combines the Multilayer Perceptron Neural Network (MLPNN) model and the Hybrid Whale Archimedes Optimization (HWAO) algorithm to enhance the classification performance on the validation data. The Marathwada dataset is selected to validate the soil nutrient prediction and classification model, and various measuring units such as cross-validation accuracy, Area Under Curve (AUC), accuracy, Mean Squared Error (MSE), G-mean, precision, specificity, and sensitivity are used for evaluation. The comparative study of this paper shows that the proposed HWAO-MLPNN achieved more classification accuracy of 98.1%, cross-validation accuracy of 98.3% for pH classification, and cross-validation accuracy of 97.9% for soil nutrient classification. The proposed model can be utilized to accurately classify soil nutrients and pH levels, which can have a significant impact on improving soil health, reducing harmful fertilizer usage, enhancing environmental quality, and ultimately increasing profitability in the agricultural sector.