Measuring wild pollinator services in agricultural production is very important in the context of sustainable management. In this study, we estimated the contribution of native pollinators to mango fruit set production of two mango cultivars Mangifera indica (L). cv. 'Sala' and 'Chok Anan'. Visitation rates of pollinators on mango flowers and number of pollen grains adhering to their bodies determined pollinator efficiency for reproductive success of the crop. Chok Anan failed to produce any fruit set in the absence of pollinators. In natural condition, we found that Sala produced 4.8% fruit set per hermaphrodite flower while Chok Anan produced 3.1% per flower. Hand pollination tremendously increased fruit set of naturally pollinated flower for Sala (>100%), but only 33% for Chok Anan. Pollinator contribution to mango fruit set was estimated at 53% of total fruit set production. Our results highlighted the importance of insect pollinations in mango production. Large size flies Eristalinus spp. and Chrysomya spp. were found to be effective pollen carriers and visited more mango flowers compared with other flower visitors.
The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.