Lentil and field pea are each commonly marketed as split and dehulled product. For plant-breeding programmes, the genetic improvement in split-yield is a targeted trait. However, the standard laboratory method for assessment of split-yield requires milled grain to be manually sorted into split and dehulled fractions. This process is time-consuming and impacts the number of germplasm lines that can be evaluated.

A machine vision approach, based on artificial neural networks, was proposed to classify split and dehulled fractions from multispectral images of grains. Three neural networks were trained on different inputs derived from the images. The networks were: (1) a convolutional network trained on the full images, (2) a convolutional network trained on distributions of image-features, and (3) a fully connected network trained on mean and standard deviation values of image-features. The accuracy and training times were compared to determine the trade-offs between training networks with smaller inputs for computational efficiency and full-image inputs for accuracy.

The networks with reduced input-data dimensionality completed network training and predictions in half the time of the image-based network. The convolutional network based on the distributions of image-features achieved a validation accuracy of 88.1%. On average, this was 1.6% greater than the image-based convolutional network and 4.6% greater than the fully connected network based on simple (mean and standard deviation) features. Feature-distributions extracted from the multispectral images captured the diversity of image data required to differentiate milling categories, leading to gains in computational efficiency over the image-based network without loss of network generality.

扁豆和豌豆通常都作为分裂和去壳产品销售。对于植物育种计划，分割产量的遗传改良是一个有针对性的性状。然而，用于评估分裂产量的标准实验室方法要求将磨碎的谷物手动分类为分裂部分和去壳部分。这个过程很耗时，并且会影响可以评估的种质系的数量。

提出了一种基于人工神经网络的机器视觉方法，对来自谷物多光谱图像的分裂和去壳部分进行分类。三个神经网络接受了来自图像的不同输入的训练。这些网络是：（1）在完整图像上训练的卷积网络，（2）在图像特征分布上训练的卷积网络，以及（3）在图像特征的均值和标准差值上训练的全连接网络。将准确性和训练时间进行比较，以确定具有较小输入的训练网络计算效率和全图像输入的准确性之间的权衡。

输入数据维数减少的网络完成网络训练和预测的时间是基于图像的网络的一半。基于图像特征分布的卷积网络实现了 88.1% 的验证准确率。平均而言，这比基于图像的卷积网络高 1.6%，比基于简单（均值和标准差）特征的全连接网络高 4.6%。从多光谱图像中提取的特征分布捕获了区分铣削类别所需的图像数据的多样性，从而在不损失网络通用性的情况下提高了基于图像的网络的计算效率。