Soil Texture Prediction Using Machine Learning Approach for Sustainable Soil Health Management

J. Abishek *

Department of Soils and Environment, Agricultural College and Research Institute, Madurai, Tamil Nadu - 625 104, India.

P. Kannan

Department of Soils and Environment, Agricultural College and Research Institute, Madurai, Tamil Nadu - 625 104, India.

M. Nirmala Devi

Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu - 625 015, India.

J. Prabhaharan

Department of Agronomy, Agricultural College and Research Institute, Madurai, Tamil Nadu - 625 104, India.

T. Sampathkumar

Department of Agronomy, Agricultural College and Research Institute, Madurai, Tamil Nadu - 625 104, India.

M. Kalpana

Agricultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu – 641 003, India.

*Author to whom correspondence should be addressed.


Abstract

Soil in the earth acts as a foothold for all crops. Soil texture is the most important soil health indicator being used for the selection of crops, mechanical manipulation, irrigation management, and fertilizer management. The texture of the soil influences the storage and flow of air and water within the soil, as well as root development, the accessibility of plant nutrients, and the activities of different microorganisms. These factors collectively impact the soil's fertility, quality, and soil health. A conventional method of soil texture analysis is cumbersome, time-consuming, and labor-intensive. Machine learning (ML) is a newly emerging technique being used to assess the soil's physical, chemical, and biological properties quickly in real-time. This is an eco-friendly approach since it does not involve any hazardous chemicals. Machine learning can learn complex features and predict nonlinear properties. Convolutional Neural Networks (CNN) employs convolutional layers to automatically learn features from the input data and is widely used in image classification, object detection, and image generation tasks in a short time. Soil texture images are given as input dataset after the completion of image subsetting, data preprocessing, and Image augmentation. This gives a CNN-based soil texture predictive model with a reliable accuracy of 87.50% at a lower cost.

Keywords: Machine learning, convolutional neural network, soil texture prediction, soil management


How to Cite

Abishek, J., P. Kannan, M. Nirmala Devi, J. Prabhaharan, T. Sampathkumar, and M. Kalpana. 2023. “Soil Texture Prediction Using Machine Learning Approach for Sustainable Soil Health Management”. International Journal of Plant & Soil Science 35 (19):1416-26. https://doi.org/10.9734/ijpss/2023/v35i193685.