Department of Agronomy, Faculty of Agronomy, Horticulture and Bioengineering, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland.
Department of Genetics and Plant Breeding, Faculty of Agronomy, Horticulture and Bioengineering, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland.
Sensors (Basel). 2023 Feb 23;23(5):2486. doi: 10.3390/s23052486.
The main objective of this study is to develop an automatic classification model for winter rapeseed varieties, to assess seed maturity and damage based on seed colour using a convolutional neural network (CNN). A CNN with a fixed architecture was built, consisting of an alternating arrangement of five classes Conv2D, MaxPooling2D and Dropout, for which a computational algorithm was developed in the Python 3.9 programming language, creating six models depending on the type of input data. Seeds of three winter rapeseed varieties were used for the research. Each imaged sample was 20.000 g. For each variety, 125 weight groups of 20 samples were prepared, with the weight of damaged or immature seeds increasing by 0.161 g. Each of the 20 samples in each weight group was marked by a different seed distribution. The accuracy of the models' validation ranged from 80.20 to 85.60%, with an average of 82.50%. Higher accuracy was obtained when classifying mature seed varieties (average of 84.24%) than when classifying the degree of maturity (average of 80.76%). It can be stated that classifying such fine seeds as rapeseed seeds is a complex process, creating major problems and constraints, as there is a distinct distribution of seeds belonging to the same weight groups, which causes the CNN model to treat them as different.
本研究的主要目的是开发一种基于卷积神经网络（CNN）的冬油菜品种自动分类模型，根据种子颜色评估种子成熟度和损伤。构建了一个具有固定架构的 CNN，由五个类 Conv2D、MaxPooling2D 和 Dropout 交替排列组成，为其在 Python 3.9 编程语言中开发了计算算法，根据输入数据的类型创建了六个模型。研究使用了三个冬油菜品种的种子。每个成像样本为 20.000 g。对于每种品种，准备了 125 个 20 个样本的重量组，损伤或不成熟种子的重量增加 0.161 g。每个重量组中的 20 个样本中的每一个都用不同的种子分布进行标记。模型验证的准确率在 80.20%至 85.60%之间，平均为 82.50%。分类成熟种子品种（平均 84.24%）的准确率高于分类成熟度（平均 80.76%）。可以说，分类像油菜籽这样的精细种子是一个复杂的过程，会产生重大问题和限制，因为属于同一重量组的种子分布明显不同，这导致 CNN 模型将它们视为不同的。
