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Yield-SpikeSegNet: An Extension of SpikeSegNet Deep-Learning Approach for the Yield Estimation in the Wheat Using Visual Images

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Title Yield-SpikeSegNet: An Extension of SpikeSegNet Deep-Learning Approach for the Yield Estimation in the Wheat Using Visual Images
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Creator Alka Arora
Sudeep Marwaha
Tanuj Mishra
Ranjeet Ranjan Jha
Mrinmoy Roy
Shailendra Kumar
Sudhir Kumar
Viswanathan Chinnusamy
 
Subject Wheat
Spikelets
Plant
 
Description Not Available
High-throughput plant phenotyping integrated with computer vision is an emerging topic in the domain of nondestructive and noninvasive plant breeding. Analysis of the emerging grain spikes and the grain weight or yield estimation in the wheat plant for a huge number of genotypes in a nondestructive way has achieved significant research attention. In this study, we developed a deep learning approach, “Yield-SpikeSegNet,” for the yield estimation in the wheat plant using visual images. Our approach consists of two consecutive modules: “Spike detection module” and “Yield estimation module.” The spike detection module is implemented using a deep encoder-decoder network for spike segmentation and output of this module is spike area and spike count. In yield estimation module, we develop machine learning models using artificial neural network and support vector regression for the yield estimation in the wheat plant. The model’s precision, accuracy, and robustness are found satisfactory in spike segmentation as 0.9982, 0.9987, and 0.9992, respectively. The spike segmentation and yield estimation performance reflect that the Yield-SpikeSegNet approach is a significant step forward in the domain of high-throughput and nondestructive wheat phenotyping.
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Date 2022-11-04T05:14:19Z
2022-11-04T05:14:19Z
2022-10-30
 
Type Article
 
Identifier Tanuj Misra, Alka Arora, Sudeep Marwaha, Ranjeet Ranjan Jha, Mrinmoy Ray, Shailendra Kumar, Sudhir Kumar & Viswanathan Chinnusamy (2022) Yield-SpikeSegNet: An Extension of SpikeSegNet Deep-Learning Approach for the Yield Estimation in the Wheat Using Visual Images, Applied Artificial Intelligence, 36:1, DOI: 10.1080/08839514.2022.2137642
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http://krishi.icar.gov.in/jspui/handle/123456789/74863
 
Language English
 
Relation Not Available;
 
Publisher Taylor & Francis