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首页> 外文会议>International Conference on Innovation, Communication and Engineering >Watermelon Recognition and Yield Estimation using Mathematical Morphology and Naïve Bayesian Classifier from Air Borne Image of Watermelon Field
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Watermelon Recognition and Yield Estimation using Mathematical Morphology and Naïve Bayesian Classifier from Air Borne Image of Watermelon Field

机译：利用数学形态学和朴素贝叶斯分类器从西瓜田空生图像中识别和鉴定西瓜

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摘要

Based on morphology and Bayesian classifier, this study proposes an efficient method to recognize and estimate the yield of watermelon from air borne image of watermelon field. In the air borne image, approximately six-hectares fields are covered. However, much noisy information such as canvas, vine, small watermelon, etc. could interfere the outcome of recognition. Therefore, this study employs three steps to overcome the problem. First, the binary image through calculating Otsu threshold from G channel of the RGB image. Second, the mathematical morphology was used to remove the information of vine. Due to keep the quality of watermelons, farmers generally adopt one plant bring one fruit. Therefore, the small size watermelons are marked specifically and removed through calculating the white-point in the second step. Finally, Bayesian classifier was used to distinguish canvas and watermelon. Following the experimental results, the recognized precision with Bayesian classifier is 95.9%, it is better than without Bayesian classifier. The execution time requires 5 minutes for each air borne image to estimate the yield of watermelon. The average accuracy of yield estimation is 89.7% and it is acceptable for farmers.
机译：基于形态学和贝叶斯分类器，本研究提出了一种有效的方法来从西瓜田间的航空图像中识别和估计西瓜的产量。在空中传播的图像中，大约覆盖了6公顷的田地。但是，许多嘈杂的信息（例如画布，藤本植物，小西瓜等）可能会干扰识别结果。因此，本研究采用三个步骤来解决该问题。首先，通过从RGB图像的G通道计算Otsu阈值来生成二值图像。其次，使用数学形态学来去除葡萄藤的信息。由于保持西瓜的品质，农民一般采用一种植物带来一种水果。因此，在第二步中，通过计算白点，可以对小尺寸的西瓜进行专门标记和去除。最后，使用贝叶斯分类器来区分帆布和西瓜。根据实验结果，使用贝叶斯分类器的识别精度为95.9％，优于不使用贝叶斯分类器的识别精度。每个空中图像的执行时间需要5分钟，以估算西瓜的产量。估计产量的平均准确度为89.7％，这对农民来说是可以接受的。

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