Development of 360-degree view imaging using L*a*b* color space for fresh fruit bunch identification

The palm oil industry is well known as a significant agricultural industry in terms of economic benefit for several tropical countries, particularly in Malaysia (Yoshizaki et al., 2013). Total amount of bunches in each of the tree is an important aspect in oil palm harvesting process. 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 360o view imaging model for bunch identification, number of Fresh Fruit Bunch (FFB) can be identified in a certain plantation area. Black bunch census was done manually to estimate yield. This can be improved by video acquisition using a high-resolution 360o camera integrated with an image processing software for video image processing to get a 360o view of each tree. Based from the standard planting pattern, it is a time consuming process to circle each tree to acquire the 360o view of each tree. In order to overcome this, a new method of data collection was established with the execution of All-Terrain Vehicle (ATV) between rows in the plantation area for video acquisition. The video recorded was processed in a software in order to construct a 360o view of each oil palm tree for further FFB identification process could be done. Image extraction was done using the processing software by referring the data from range sensor installed at the ATV throughout the data collection process in the oil palm plantation. After useful image were extracted, MATLAB software was programmed to process all the selected images for the detection of FFB of each tree. Image pre-processing was conducted where errors in the image were corrected to detect the oil palm bunches. In order to present an appropriate format of image processing system, the RGB images were converted into grayscale images. Image segmentation was done based on a threshold value of L*a*b* to separate between canopy and trunk, fruit bunches and background image. The features were extracted from each pixel of the RGB image. As a result, a new method for video acquisition is established as well as a processing method for bunch counting for large scale plantation area. In this research, mean value for L*a*b* color space was determined by using 90 images samples for image threshold in order to identify the FFB on tree crown. Using L*a*b* color space, image was threshold to identify black and both red and black FFB. In this research, image verification was done by using the mean L*a*b* value for black bunch identification. Model threshold verification for 48 samples of images resulted with Coefficient of Determination, R2 of 0.8029 to identify black bunch on each tree crown. The outcome for this research will help to fully automate the process for bunch identification in the future.

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