Hinting Pipeline and Multivariate Regression CNN for Maize Kernel
Counting on the Ear
- URL: http://arxiv.org/abs/2306.06553v1
- Date: Sun, 11 Jun 2023 00:58:38 GMT
- Title: Hinting Pipeline and Multivariate Regression CNN for Maize Kernel
Counting on the Ear
- Authors: Felipe Ara\'ujo, Igor Gadelha, Rodrigo Tsukahara, Luiz Pita, Filipe
Costa, Igor Vaz, Andreza Santos and Guilherme F\^olego
- Abstract summary: We propose a novel preprocessing pipeline named hinting that guides the attention of the model to the center of the corn kernels and enables a deep learning model to deliver better performance.
Experiments indicated that the proposed approach excels the current manual estimates, obtaining MAE of 34.4 and R2 of 0.74 against 35.38 and 0.72 for the manual estimate, respectively.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Maize is a highly nutritional cereal widely used for human and animal
consumption and also as raw material by the biofuels industries. This
highlights the importance of precisely quantifying the corn grain productivity
in season, helping the commercialization process, operationalization, and
critical decision-making. Considering the manual labor cost of counting maize
kernels, we propose in this work a novel preprocessing pipeline named hinting
that guides the attention of the model to the center of the corn kernels and
enables a deep learning model to deliver better performance, given a picture of
one side of the corn ear. Also, we propose a multivariate CNN regressor that
outperforms single regression results. Experiments indicated that the proposed
approach excels the current manual estimates, obtaining MAE of 34.4 and R2 of
0.74 against 35.38 and 0.72 for the manual estimate, respectively.
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