{"title":"Development of 360-degree imaging system for fresh fruit bunch (FFB) identification","authors":"","doi":"10.37865/jafe.2021.0028","DOIUrl":null,"url":null,"abstract":"In every cycle of harvesting operation, farmer does not have any information on how many bunches and which oil palm tree will be harvested. By introducing the 360ᵒ camera imaging system, number of Fresh Fruit Bunch (FFB) can be determined for every tree in a plantation area. Black bunch census was done manually to estimate yield. This was improved by video acquisition using a high resolution 360ᵒ camera integrated with an image processing software for video image processing to calculate number of FFB. Based on the standard planting pattern, it is time consuming process to circle each tree to acquire the 360ᵒ view of each tree. Current technology to approach bunches is destructive and conventional since the process involve physical contact between workers and FFB. Thus, a new method was established by the execution of All-Terrain Vehicle (ATV) between rows in plantation area for video acquisition. Images were extracted and threshold by using MATLAB software. L*, a*, and b* color space was used for the bunch identification throughout 90 samples of images to identify the mean intensity value. Model threshold verification for another 48 samples of images resulted with Coefficient of Determination, R2 of 0.8029 for bunch identification. As a result, a new method for video acquisition was established as well as processing method for bunch identification for large scale plantation area.","PeriodicalId":23659,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Biological, Biomolecular, Agricultural, Food and Biotechnological Engineering","volume":"18 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Academy of Science, Engineering and Technology, International Journal of Biological, Biomolecular, Agricultural, Food and Biotechnological Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37865/jafe.2021.0028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

In every cycle of harvesting operation, farmer does not have any information on how many bunches and which oil palm tree will be harvested. By introducing the 360ᵒ camera imaging system, number of Fresh Fruit Bunch (FFB) can be determined for every tree in a plantation area. Black bunch census was done manually to estimate yield. This was improved by video acquisition using a high resolution 360ᵒ camera integrated with an image processing software for video image processing to calculate number of FFB. Based on the standard planting pattern, it is time consuming process to circle each tree to acquire the 360ᵒ view of each tree. Current technology to approach bunches is destructive and conventional since the process involve physical contact between workers and FFB. Thus, a new method was established by the execution of All-Terrain Vehicle (ATV) between rows in plantation area for video acquisition. Images were extracted and threshold by using MATLAB software. L*, a*, and b* color space was used for the bunch identification throughout 90 samples of images to identify the mean intensity value. Model threshold verification for another 48 samples of images resulted with Coefficient of Determination, R2 of 0.8029 for bunch identification. As a result, a new method for video acquisition was established as well as processing method for bunch identification for large scale plantation area.
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鲜果串360度成像系统的研制
在每一个收获周期中,农民并不知道要收获多少束油棕和哪棵油棕。通过引入360°摄像机成像系统,可以确定种植区域内每棵树的鲜果串(FFB)数量。黑束普查是手工进行的,以估计产量。采用高分辨率360°摄像机进行视频采集,并集成了用于视频图像处理的图像处理软件来计算FFB的数量,从而改善了这一点。根据标准的种植模式,将每棵树绕一圈以获得每棵树360°的视角是一个耗时的过程。目前接近束的技术是破坏性的和传统的,因为这个过程需要工人和FFB之间的物理接触。为此,建立了一种利用全地形车(ATV)在种植区行间执行视频采集的新方法。利用MATLAB软件对图像进行提取和阈值处理。使用L*, a*, b*色彩空间对90个图像样本进行束识别,识别平均强度值。对另外48个样本的图像进行模型阈值验证,其决定系数R2为0.8029。建立了一种新的视频采集方法和大规模种植区束识别的处理方法。
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