Machine vision assessment of mango orchard flowering

作者：Wang Zhenglin; Underwood James; Walsh Kerry B^*

来源：Computers and Electronics in Agriculture, 2018, 151: 501-511.

DOI：10.1016/j.compag.2018.06.040

Machine vision assessment of mango orchard flowering involves detection of an inflorescence (a panicle) with flowers at various stages of development. Two systems were adopted contrasting in camera, illumination hardware and image processing. The image processing paths were: (i) colour thresholding of pixels followed by SVM classification to estimate inflorescence associated pixel number (panicle area), and panicle area relative to total canopy area (Towering intensity') using two images per tree ('dual view'), and (ii) a faster R-CNN for panicle detection, using either 'dual-view' or 'multi-view' tracking of panicles between consecutive images to achieve a panicle count per tree. The correlation coefficient of determination between the machine vision flowering intensity and area estimate (path i) and in field human visual counts of panicles (past 'asparagus' stage) per tree was 0.69 and 0.81, while that between the machine vision (path ii) and human panicle count per tree was 0.78 and 0.84 for the dual and multi-view detection approaches, respectively (n = 24), while that for repeat human counts was 0.86. The use of such information is illustrated in context of (i) monitoring the time of peak flowering based on repeated measures of flowering intensity, for use as the start date within heat sum models of fruit maturation, (ii) identification and mapping of early flowering trees to enable selective early harvest and (iii) exploring relationships between flowering and fruit yield. For the current orchard and season, the correlation coefficient of determination between machine vision estimates of panicle area and multi-view panicle count and fruit yield per tree was poor (R-2 of 0.19 and 0.28, respectively, n = 44), indicative of variable fruit set per panicle and retention between trees.

• 出版日期2018-8