Abstract
Agriculture is the backbone of Indian production which is the vital sector for food production. It is very important for national government to know what type of crops are being grown in which region for budget planning to import and export food products. Traditional ground survey method is laborious, time-consuming, and expensive. Along with this, continuous monitoring of crops is highly difficult. Crop area estimation is a key element in crop production forecasting and estimation. Crop classification and mapping are the most challenging tasks among the land use/land cover classification problems.
In agriculture domain, the common approach used by the government (farmers) for crop monitoring is to go to the field and acquire the images using cameras for estimation of the crop yield. So in this context, a fast, reliable, and automated system is required which provides the exact crop mapping using satellite images. In recent years, crop identification and area monitoring from satellite images are given more and more attention.
The stages are image acquisition, image preprocessing, feature extraction, and image classification. Satellite images are preprocessed and features are extracted from input images. Based on the features extracted, images are classified based on the extracted features. The proposed automated system should provide better accuracy than the existing in the literature.
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Kalaivani, A., Khilar, R. (2020). Crop Classification and Mapping for Agricultural Land from Satellite Images. In: Hemanth, D. (eds) Artificial Intelligence Techniques for Satellite Image Analysis. Remote Sensing and Digital Image Processing, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-030-24178-0_10
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