Machine Learning Approaches for Soil Image Classification: A Systematic Review


Authors : Girish D. Chate; S. S. Bhamare

Volume/Issue : Volume 10 - 2025, Issue 4 - April


Google Scholar : https://tinyurl.com/3yudk3sh

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DOI : https://doi.org/10.38124/ijisrt/25apr045

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Abstract : Classification of soil images is a universal problemThe world’s population is rapidly increasing, and so is the need for food. Farmers’ traditional techniques are insufficient to fulfil increasing demand, causing them to halt soil cultivation. Farmers must understand the best soil type for a certain crop to optimize agricultural output and meet growing food demand. There are several soil laboratory and field procedures for soil classification, but each has its own set of restrictions, including time and labour requirements. Computer-based soil image categorization approaches required for helping farmers on their farms. This survey paper discusses different computer-based soil image classification methods.

Keywords : Machine Learning, Soil Classification, Texture, Color, CNN, PCA.

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Classification of soil images is a universal problemThe world’s population is rapidly increasing, and so is the need for food. Farmers’ traditional techniques are insufficient to fulfil increasing demand, causing them to halt soil cultivation. Farmers must understand the best soil type for a certain crop to optimize agricultural output and meet growing food demand. There are several soil laboratory and field procedures for soil classification, but each has its own set of restrictions, including time and labour requirements. Computer-based soil image categorization approaches required for helping farmers on their farms. This survey paper discusses different computer-based soil image classification methods.

Keywords : Machine Learning, Soil Classification, Texture, Color, CNN, PCA.

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