Authors :
Girish D. Chate; S. S. Bhamare
Volume/Issue :
Volume 10 - 2025, Issue 4 - April
Google Scholar :
https://tinyurl.com/3yudk3sh
Scribd :
https://tinyurl.com/fcx9srjw
DOI :
https://doi.org/10.38124/ijisrt/25apr045
Google Scholar
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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.
References :
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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.