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Intelligent organic matter prediction of agriculture soil using satellite images

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Abstract

Soil Organic Matter (SOM) is one of the significant features in soil management. It provides essential information to improve the nutrients of the crops and increase the soil properties in agriculture. In addition, the connection between satellite images and SOM properties is necessary for the scope of agriculture. The present study focuses on an intelligent organic matter prediction by the proposed Coati Convolutional-based Organic Prediction (CCNbOP) System. It helps to analyze the organic matter content in the particular soil with the help of satellite images with soil index data. The data is gathered and injected into the designed framework for training and testing. Thus, it processes the subsequent stages and provides excellent prediction in the soil. After prediction, two soil types were classified, and performance was estimated. Finally, the designed model achieves higher accuracy, precision, recall, and f-measure values of 97.87% accordingly for organic matter prediction in satellite images of Medechal district, Telangana. Also achieves lower RMSE and MAE values. Moreover, the R2 value is higher than in other systems. Thus, the performance is analyzed and compared with the existing models to encourage good performance in the outcome.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Mallekedi Anand, Dr. Anuj Jain, and Dr. Manoj Kumar Shukla have contributed equally to the work.

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Correspondence to Mallekedi Anand.

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Anand, M., Jain, A. & Shukla, M.K. Intelligent organic matter prediction of agriculture soil using satellite images. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18955-w

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