ABSTRACT Palm oil is a major contributor to Malaysia’s GDP in the agriculture sector. The sheer vastness of oil palm plantations requires a huge effort to administer. An oil palm plantation in regards to the irrigation process, fertilization, and planning for planting new trees require an audit process to correctly count the oil palm trees. Currently, the audit is done manually using aerial view images. Therefore, an effective and efficient method is imperative. This paper proposes a new automatic end-to-end method based on deep learning (DL) for detection and counting oil palm trees from images obtained from unmanned aerial vehicle (UAV) drone. The acquired images were first cropped and sampled into small size of sub-images, which were divided into a training set, a validation set, and a testing set. A DL algorithm based on Faster-RCNN was employed to build the model, extracts features from the images and identifies the oil palm trees, and gives information on the respective locations. The model was then trained and used to detect individual oil palm tree based on data from the testing set. The overall accuracy of oil palm tree detection was measured from three different sites with 97.06%, 96.58%, and 97.79% correct oil palm detection. The results show that the proposed method is more effective, accurate detection, and correctly counts the number of oil palm trees from the UAV images.

中文翻译：

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基于深度学习方法的无人机图像油棕树自动检测

摘要 棕榈油是马来西亚农业部门 GDP 的主要贡献者。巨大的油棕种植园需要付出巨大的努力来管理。油棕种植园在灌溉过程、施肥和种植新树的规划方面需要审核过程，以正确计算油棕树的数量。目前，审计是使用鸟瞰图像手动完成的。因此，一种有效且高效的方法势在必行。本文提出了一种新的基于深度学习 (DL) 的自动端到端方法，用于从无人机 (UAV) 无人机获得的图像中检测和计数油棕树。获取的图像首先被裁剪和采样成小尺寸的子图像，这些子图像分为训练集、验证集和测试集。采用基于 Faster-RCNN 的 DL 算法来构建模型，从图像中提取特征并识别油棕树，并提供有关各个位置的信息。然后训练该模型并用于根据来自测试集的数据检测单个油棕树。油棕树检测的整体准确度是从三个不同地点测量的，油棕检测正确率分别为 97.06%、96.58% 和 97.79%。结果表明，所提出的方法更有效，检测准确，并正确统计了无人机图像中油棕树的数量。油棕树检测的整体准确度是从三个不同地点测量的，油棕检测正确率分别为 97.06%、96.58% 和 97.79%。结果表明，所提出的方法更有效、检测准确，并且从无人机图像中正确统计了油棕树的数量。油棕树检测的整体准确度是从三个不同地点测量的，油棕检测正确率分别为 97.06%、96.58% 和 97.79%。结果表明，所提出的方法更有效、检测准确，并且从无人机图像中正确统计了油棕树的数量。