This paper presents maturity classification algorithms developed for small datasets and methods to deal with the highly variable and continuously changing agricultural environment. The algorithms were applied to the maturity classification of red and yellow sweet peppers, with data acquired from two different datasets, including 296 images. The maturity classification achieved 98.2 % and 97.3 % accuracy for classifying into two classes, between mature and immature classes of red and yellow peppers, respectively, and 89.5 % and 97.3 % accuracy for classifying into four maturity classes. The random forest algorithm is very robust and incurs a low computational cost, and therefore is recommended for the highly variable agricultural domain.

An improvement of 28.65 % in classification accuracy was achieved by applying the methods developed for adapting to new datasets.

本文介绍了针对小型数据集开发的成熟度分类算法和方法，以应对变化多端，不断变化的农业环境。将该算法应用于红色和黄色甜椒的成熟度分类，其数据来自两个不同的数据集，包括296张图像。成熟度分类将红辣椒和黄椒分为两类，分别分为成熟和未成熟两类，准确度分别为98.2％和97.3％，而分为四个成熟度分类的准确度为89.5％和97.3％。随机森林算法非常健壮，并且计算成本较低，因此建议将其用于高度可变的农业领域。

通过应用为适应新数据集而开发的方法，分类精度提高了28.65％。