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<p>Crop selection and soil texture are crucial components of agricultural production. The existing methods faced more complexities in accurately predicting the soil texture and crops. Also, the technique degraded in prediction accuracy. So, in this work, a novel Buffalo-based Pyramidal net Mechanism (BbPM) is designed to predict suitable crops and soil texture. The satellite image data was initially collected; the image contains more noise, maximizing the complexity and increasing the error rate. Hence, filter the noise characteristics from the dataset. Moreover, the soil features were extracted with the help of the buffalo tracking function. The soil texture was predicted based on soil features like temperature, humidity, pH, and rainfall, and then suitable crops were selected based on the soil type. The feature analysis and prediction function were carried out based on the fitness process of the method. Finally, the robustness of the BbPM is determined concerning accuracy, f1-score, Recall, Precision, and error.</p>

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Intelligent approach for soil texture analysis and suitable crop selection using satellite images

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Kavitha Nallamothu,
  • Ragavamsi Davuluri,
  • K. Ashok Reddy,
  • Shaik Salma Begum,
  • Adilakshmi Yannam,
  • Keerthi Guttikonda,
  • T. Naga Mani

摘要

Crop selection and soil texture are crucial components of agricultural production. The existing methods faced more complexities in accurately predicting the soil texture and crops. Also, the technique degraded in prediction accuracy. So, in this work, a novel Buffalo-based Pyramidal net Mechanism (BbPM) is designed to predict suitable crops and soil texture. The satellite image data was initially collected; the image contains more noise, maximizing the complexity and increasing the error rate. Hence, filter the noise characteristics from the dataset. Moreover, the soil features were extracted with the help of the buffalo tracking function. The soil texture was predicted based on soil features like temperature, humidity, pH, and rainfall, and then suitable crops were selected based on the soil type. The feature analysis and prediction function were carried out based on the fitness process of the method. Finally, the robustness of the BbPM is determined concerning accuracy, f1-score, Recall, Precision, and error.