Hinting Pipeline and Multivariate Regression CNN for Maize Kernel
  Counting on the Ear
Felipe Araújo Igor Gadelha Rodrigo Tsukahara Luiz Pita Filipe Costa Igor Vaz Andreza Santos Guilherme Fôlego
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.