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SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques

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Title SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques
Not Available
 
Creator Arpan K. Maji
Sudeep Marwaha
Sudhir Kumar
Alka Arora
Viswanathan Chinnusamy
Shahnawazul Islam
 
Subject SlypNet
Yield Estimation
plant phenotyping
Computer vision
deep learning
spike detection
segmentation
spikelet detection,
 
Description Not Available
The application of computer vision in agriculture has already contributed immensely to restructuring the existing field practices starting from the sowing to the harvesting. Among the different plant parts, the economic part, the yield, has the highest importance and becomes the ultimate goal for the farming community. It depends on many genetic and environmental factors, so this curiosity about knowing the yield brought several precise pre-harvest prediction methods using different ways. Out of those techniques, non-invasive yield prediction techniques using computer vision have been proved to be the most efficient and trusted platform. This study developed a novel methodology, called SlypNet, using advanced deep learning networks, i.e., Mask R-CNN and U-Net, which can extract various plant morphological features like spike and spikelet from the visual image of the wheat plant and provide a high-throughput yield estimate with great precision. Mask R-CNN outperformed previous networks in spike detection by its precise detection performance with a mean average precision (mAP) of 97.57%, a F1 score of 0.67, and an MCC of 0.91 by overcoming several natural field constraints like overlapping and background interference, variable resolution, and high bushiness of plants. The spikelet detection module’s accuracy and consistency were tested with about 99% validation accuracy of the model and the least error, i.e., a mean square error of 1.3 from a set of typical and complex views of wheat spikes. Spikelet yield cumulatively showed the probable production capability of each plant. Our method presents an integrated deep learning platform of spikelet-based yield prediction comprising spike and spikelet detection, leading to higher precision over the existing methods.
Not Available
 
Date 2022-09-08T09:39:07Z
2022-09-08T09:39:07Z
2022-08-04
 
Type Research Paper
 
Identifier Not Available
https://doi.org/10.3389/fpls.2022.889853
http://krishi.icar.gov.in/jspui/handle/123456789/74051
 
Language English
 
Relation Not Available;
 
Publisher Frontiers