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• W4285243265 abstract "Analysis of crop quality from image data is a multi-domain task, which includes effective image capture, pre-processing, fusion, segmentation, feature extraction, selection of optimum features, classification and post-processing. In order to perform these tasks with utmost efficiency, several architectures have been proposed by researchers and satellite image processing system designers. These architectures vary widely in terms of performance parameters like accuracy, response time, applicability to different types of imagery, image data requirement, etc. For instance, architectures that are well suited for cotton crop analysis do not give good accuracy for other crop types, while generalized architectures that have high accuracy, underperform when applied to specialized crop types. Moreover, selection of algorithms at micro-level is also difficult due to a large variety of architectures available for each of the internal tasks. In order to reduce this ambiguity, the underlying text analyzes various state-of-the-art recent models and architectures that are proposed for high efficiency crop analysis using satellite image processing. The analysis is supported by statistical comparison and recommendations for these architectures w.r.t. their field of application, and desired system performance capabilities. This text also recommends optimizations to the reviewed architectures in order to further improve their parametric performance and large scale deploy-ability." @default.
• W4285243265 created "2022-07-14" @default.
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• W4285243265 date "2022-01-01" @default.
• W4285243265 modified "2023-10-14" @default.
• W4285243265 title "Empirical Study of Far-Field Crop Quality Examination Models: A Numerical Outlook" @default.
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• W4285243265 doi "https://doi.org/10.1007/978-981-19-0836-1_1" @default.
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