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首页> 外文期刊>PLoS One >Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)
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Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)

机译：基于深度学习的香蕉植物检测和使用从无人机飞行器（UAV）收集的高分辨率红绿蓝（RGB）图像计数

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The production of banana—one of the highly consumed fruits—is highly affected due to loss of certain number of banana plants in an early phase of vegetation. This affects the ability of farmers to forecast and estimate the production of banana. In this paper, we propose a deep learning (DL) based method to precisely detect and count banana plants on a farm exclusive of other plants, using high resolution RGB aerial images collected from Unmanned Aerial Vehicle (UAV). An attempt to detect the plants on the normal RGB images resulted less than 78.8% recall for our sample images of a commercial banana farm in Thailand. To improve this result, we use three image processing methods—Linear Contrast Stretch, Synthetic Color Transform and Triangular Greenness Index—to enhance the vegetative properties of orthomosaic, generating multiple variants of orthomosaic. Then we separately train a parameter-optimized Convolutional Neural Network (CNN) on manually interpreted banana plant samples seen on each image variants, to produce multiple results of detection on our region of interest. 96.4%, 85.1% and 75.8% of plants were correctly detected on three of our dataset collected from multiple altitude of 40, 50 and 60 meters, of same farm. Further discussion on results obtained from combination of multiple altitude variants are also discussed later in the research, in an attempt to find better altitude combination for data collection from UAV for the detection of banana plants. The results showed that merging the detection results of 40 and 50 meter dataset could detect the plants missed by each other, increasing recall upto 99%.
机译：香蕉 - 香蕉 - 一体的脾气院 - 由于植被早期阶段的一定数量的香蕉植物而受到高度影响。这影响农民预测和估计香蕉生产的能力。在本文中，我们提出了一种基于深度学习（DL）的方法，以便使用从无人机（UAV）收集的高分辨率RGB航空图像，精确地检测和计数不包括其他植物的农场。试图检测普通RGB图像上的植物导致我们在泰国商业香蕉农场的样本图像召回的召回量小于78.8％。为了提高这一结果，我们使用三个图像处理方法 - 线性对比度拉伸，合成彩色变换和三角形绿色指数 - 增强正畸的营养性质，产生正畸的多种变体。然后，我们将参数优化的卷积神经网络（CNN）分开地在每种图像变体上观察到的手动解释的香蕉植物样本，以产生对我们感兴趣区域的检测结果。在来自同一农场的多个海拔40,50和60米的三个地区的三个数据集中，正确检测到96.4％，85.1％和75.8％。进一步讨论了从多个高度变体组合获得的结果也在研究中讨论，试图找到从无人机的数据收集的更好的高度组合，以检测香蕉植物。结果表明，合并40米和50米数据集的检测结果可能会检测到彼此错过的植物，增加召回高达99％。

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