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Maize is a highly nutritional cereal widely used for human and animalconsumption and also as raw material by the biofuels industries. Thishighlights the importance of precisely quantifying the corn grain productivityin season, helping the commercialization process, operationalization, andcritical decision-making. Considering the manual labor cost of counting maizekernels, we propose in this work a novel preprocessing pipeline named hintingthat guides the attention of the model to the center of the corn kernels andenables a deep learning model to deliver better performance, given a picture ofone side of the corn ear. Also, we propose a multivariate CNN regressor thatoutperforms single regression results. Experiments indicated that the proposedapproach excels the current manual estimates, obtaining MAE of 34.4 and R2 of0.74 against 35.38 and 0.72 for the manual estimate, respectively.

Hinting Pipeline and Multivariate Regression CNN for Maize Kernel Counting on the Ear