Smart soil image classification system using lightweight convolutional neural network

doi:10.1016/j.eswa.2023.122185

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Smart soil image classification system using lightweight convolutional neural network

2024-03-01

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D. N. Kiran Pandiri

Tripti Goel

DOI：10.1016/j.eswa.2023.122185

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Abstract

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In the agriculture sector, soil classification plays a significant task, as it helps in soil tillage, crop selection, moisture level estimation, and automation. Conventionally, soil classification is carried out with the help of physical, chemical, and biological characteristics of the geo-referenced and mapped soil. Soil classification by conventional and laboratory methods is time-consuming, high-cost, and requires proficiency. This study presents a quick and cost-effective prediction of soil type by using soil images. A soil image dataset has been created to classify the soil type using images. To create the soil image dataset, 392 soil samples are collected from different agricultural fields in Andhra Pradesh, India. The collected samples are dried and the soil type is identified using a sieve and hydrometer analysis in the laboratory. An imaging setup has been made to capture the images of the dried soil samples using a smartphone camera. The captured images are pre-processed using: RGB extraction, and V extraction from HSV bins, and adaptive histogram are applied to highlight the texture features of the soil images. A novel lightweight convolutional neural network called Light-SoilNet is proposed to classify five soil sample images: sand, clay, loam, loamy sand, and sandy loam. The proposed network is designed to take care of the imbalanced soil image dataset. The proposed network is tested and compared with state-of-the-art lightweight and pre-trained deep learning networks. The proposed Light-SoilNet network architecture has produced an overall accuracy of 97.2% in classifying the soils. The comparison of the results shows the performance of the proposed model using the image and deep learning techniques in classifying the soil types.

Keywords:

Soil type(s)

Light-SoilNet

Soil classification

Deep learning

Artificial intelligence

Hydrometer test