School of Computing & Mathematics, Charles Sturt University, Wagga Wagga 2678, Australia.
CM3 Research Centre, Charles Sturt University, Bathurst 2795, Australia.
Sensors (Basel). 2017 May 23;17(6):1186. doi: 10.3390/s17061186.
This paper presents an investigation of natural inspired intelligent computing and its corresponding application towards visual information processing systems for viticulture. The paper has three contributions: (1) a review of visual information processing applications for viticulture; (2) the development of natural inspired computing algorithms based on artificial immune system (AIS) techniques for grape berry detection; and (3) the application of the developed algorithms towards real-world grape berry images captured in natural conditions from vineyards in Australia. The AIS algorithms in (2) were developed based on a nature-inspired clonal selection algorithm (CSA) which is able to detect the arcs in the berry images with precision, based on a fitness model. The arcs detected are then extended to perform the multiple arcs and ring detectors information processing for the berry detection application. The performance of the developed algorithms were compared with traditional image processing algorithms like the circular Hough transform (CHT) and other well-known circle detection methods. The proposed AIS approach gave a Fscore of 0.71 compared with Fscores of 0.28 and 0.30 for the CHT and a parameter-free circle detection technique (RPCD) respectively.
本文研究了自然启发式智能计算及其在葡萄栽培视觉信息处理系统中的应用。本文有三个贡献：（1）对葡萄栽培视觉信息处理应用的综述；（2）基于人工免疫系统（AIS）技术开发用于葡萄检测的自然启发式计算算法；（3）将开发的算法应用于从澳大利亚葡萄园在自然条件下拍摄的真实葡萄浆果图像。（2）中 AIS 算法是基于一种受自然启发的克隆选择算法（CSA）开发的，该算法能够根据一个适合度模型精确检测浆果图像中的弧。然后，检测到的弧被扩展为执行多个弧和环检测器的信息处理，以实现浆果检测应用。与传统的图像处理算法（如圆形霍夫变换（CHT）和其他著名的圆检测方法）相比，开发的算法的性能进行了比较。所提出的 AIS 方法的 F 分数为 0.71，而 CHT 的 F 分数分别为 0.28 和 0.30，RPCD（一种无参数的圆检测技术）的 F 分数为 0.30。
