In the field of agriculture science, automatic visual inspection improves the commercial, quality and fertility of the country. It is very challenging to sort the fruit based on quality because of varieties of fruits available in the market. Human grades the fruit but it is inconsistent, stagnant, and expensive and influenced by the surrounding. Thus an effective system for grading of fruit is desired. In this paper, an automated fruit grading system is developed for apple to classify based on external quality. The different combination of several features are considered depending on the damages exposed on apple fruits. In this work, these features are considered as input to train Support Vector Machine (SVM). The classifier has been contemplated with two different database of apple: one having 100 color images out of which 24 are of apples with various defects and the other dataset having 112 color images out of which 56 are of apples with various defects. The system performance has been validated using k-fold cross validation technique by considering different values of k. The maximum accuracy 96.81% and 93.00% for two dataset respectively, achieved by the system is encouraging and is comparable with the state of art techniques.

在农业科学领域，自动外观检查可改善该国的商业，质量和肥力。由于市场上出售的水果种类繁多，因此根据质量对水果进行分类非常具有挑战性。人类对这种水果进行分级，但这种水果前后不一致，停滞不前且价格昂贵，并受周围环境的影响。因此，需要一种有效的水果分级系统。本文针对苹果开发了一种自动水果分级系统，以根据外部质量进行分类。根据苹果果实受到的损害，可以考虑几种功能的不同组合。在这项工作中，这些功能被视为训练支持向量机（SVM）的输入。苹果的两个不同数据库已经考虑到了分类器：一个具有100幅彩色图像，其中24幅是有各种缺陷的苹果，另一个数据集是112幅彩色图像，其中56幅是有各种缺陷的苹果。通过考虑k的不同值，已使用k倍交叉验证技术验证了系统性能。该系统分别实现的两个数据集的最大准确度分别为96.81％和93.00％，这令人鼓舞，并且与现有技术水平相当。