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.

本文提出了一种名为 hinting 的新颖预处理流水线，指导了模型对玉米籽粒中心的关注，并使用多元 CNN 回归器提高了性能，超过了手动预测的估计值，从而提高了商业化过程、流程操作和关键决策的重要性。

玉米穗上麦粒计数的 Hinting Pipeline 和多元回归 CNN

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

Manual determination of plant phenotypic properties such as plant
architecture, growth, and health is very time consuming and sometimes
destructive. Automatic image analysis has become a popular approach. This
research aims to identify the position (and number) of leaves from a temporal
sequence of high-quality indoor images consisting of multiple views, focussing
in particular of images of maize. The procedure used a segmentation on the
images, using the convex hull to pick the best view at each time step, followed
by a skeletonization of the corresponding image. To remove skeleton spurs, a
discrete skeleton evolution pruning process was applied. Pre-existing
statistics regarding maize development was incorporated to help differentiate
between true leaves and false leaves. Furthermore, for each time step, leaves
were matched to those of the previous and next three days using the
graph-theoretic Hungarian algorithm. This matching algorithm can be used to
both remove false positives, and also to predict true leaves, even if they were
completely occluded from the image itself. The algorithm was evaluated using an
open dataset consisting of 13 maize plants across 27 days from two different
views. The total number of true leaves from the dataset was 1843, and our
proposed techniques detect a total of 1690 leaves including 1674 true leaves,
and only 16 false leaves, giving a recall of 90.8%, and a precision of 99.0%.

该文章通过自动图像分析的方法，借助凸包、骨架化和匈牙利算法等技术，准确地检测出玉米的叶片数目和位置，并成功预测了部分被遮挡的叶片，取得了 90.8% 的召回率和 99.0% 的精确率。