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

TL;DR

This paper introduces a novel hinting preprocessing pipeline and a multivariate CNN regressor for accurately counting maize kernels from images, significantly improving over manual counting methods.

Contribution

The work presents a new hinting pipeline to focus model attention and a multivariate CNN approach, enhancing maize kernel counting accuracy from images.

Findings

MAE of 34.4, outperforming manual estimate MAE of 35.38

R2 score of 0.74, higher than manual estimate 0.72

Proposed method surpasses manual counting accuracy

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.