Crop classification in early phenological stages has been a difficult task due to spectrum similarity of different crops. For this purpose, low altitude platforms such as drones have great potential to provide high resolution optical imagery where Machine Learning (ML) applied to classify different types of crops. In this research work, crop classification is performed at different phenological stages using optical images which are obtained from drone. For this purpose, gray level co-occurrence matrix (GLCM) based features are extracted from underlying gray scale images collected by the drone. To classify the different types of crops, different ML algorithms including Random Forest (RF), Naive Bayes (NB), Neural Network (NN) and Support Vector Machine (SVM) are applied. The results showed that the ML algorithms performed much better on GLCM features as compared to gray scale images with a margin of 13.65% in overall accuracy.

中文翻译：

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使用低空遥感平台的基于灰度共生矩阵（GLCM）纹理的农作物分类

由于不同作物的光谱相似性，物候早期阶段的作物分类一直是一项艰巨的任务。为此，无人机等低海拔平台具有提供高分辨率光学图像的巨大潜力，其中机器学习（ML）用于对不同类型的农作物进行分类。在这项研究工作中，使用从无人机获得的光学图像，在不同物候阶段对作物进行分类。为此，从无人机收集的基础灰度图像中提取基于灰度共现矩阵（GLCM）的特征。为了对不同类型的农作物进行分类，应用了不同的机器学习算法，包括随机森林（RF），朴素贝叶斯（NB），神经网络（NN）和支持向量机（SVM）。