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Impact of Vegetation Indices on Wheat Yield Prediction Using Spatio-Temporal Modeling

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Digital Ecosystem for Innovation in Agriculture

Part of the book series: Studies in Big Data ((SBD,volume 121))

Abstract

Precise yield prediction is necessary for any Government to design and implement agriculture-related policy. Usually, remotely sensed images are used for prediction, and it is a complex task with dependence on many parameters like weather, soil, and farm practices. The fusion of extra information can improve the prediction. Therefore, the chapter studies the impact of vegetation indices on wheat yield prediction using satellite images. The chapter uses convolutional neural network (CNN) to extract the spatial features, which are then fed into the long short-term memory (LSTM) to derive the temporal information. They are subsequently fed into a fully connected network (FCN) to predict the yield. The chapter demonstrates that adding information about vegetation indices improves yield prediction.

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Acknowledgements

We thank Dr. Srikrishnan Divakaran from Ahmedabad University for providing valuable input and guidance. In addition, the authors heartfully thank L D College of Engineering and Ahmedabad University for providing the computing facility.

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Authors and Affiliations

  1. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, India

    Pragnesh Patel, Maitrik Shah, Mehul S. Raval & Sanjay Chaudhary

  2. L. D. College of Engineering, Ahmedabad, India

    Hasit Parmar

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  1. Pragnesh Patel
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  2. Maitrik Shah
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  3. Mehul S. Raval
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  4. Sanjay Chaudhary
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  5. Hasit Parmar
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Correspondence to Pragnesh Patel .

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Editors and Affiliations

  1. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Sanjay Chaudhary

  2. CIFOR-ICRAF India, Asia Continental Program, New Delhi, Delhi, India

    Chandrashekhar M. Biradar

  3. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Srikrishnan Divakaran

  4. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Mehul S. Raval

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Patel, P., Shah, M., Raval, M.S., Chaudhary, S., Parmar, H. (2023). Impact of Vegetation Indices on Wheat Yield Prediction Using Spatio-Temporal Modeling. In: Chaudhary, S., Biradar, C.M., Divakaran, S., Raval, M.S. (eds) Digital Ecosystem for Innovation in Agriculture. Studies in Big Data, vol 121. Springer, Singapore. https://doi.org/10.1007/978-981-99-0577-5_10

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