Boundary Detection of Pigs in Pens Based on Adaptive Thresholding Using an Integral Image and Adaptive Partitioning

Abstract

The seed of rice (Oryza sativa L.) from the highlands of northern Thailand, which is located within the species' centre of diversity, constitutes some of the world's last local rice germplasm still retained on-farm, provides local farmers and communities with a readily accessible resource, and is a source of value-adding traits for rice breeding. This paper reports on the germplasm represented by 281 seed samples collected in 2013 from an area of the highlands between latitudes 17.76°N to 20.18°N and longitudes 97.76°E to 100.48°E. The samples were provided by farmers belonging to 10 ethnicities, in number that closely correlated with the groups' share of the highland population (r = 0.84; P < 0.01). Compared with the slender grain rice of the lowlands, the highland germplasm was distinctive in its grain shape, and classed as large grain type in the husk, and medium grain type as de-husked, brown rice. The rice, which was predominantly of non-glutinous grain type and grown mainly as upland rice, had generally higher iron concentrations than rice in the lowlands; thus demonstrating how an on-farm rice germplasm may directly benefit local farmers and communities who consume the rice they grow. In addition, potential value-adding traits were identified in varieties and seed samples with the highest zinc density and novel rice with pigmented pericarp and high anti-oxidative capacity.

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

“…Their method used adaptive elliptic block and wavelet edge detection after performing two-dimensional Otsu threshold segmentation. To investigate pig boundary detection in dirty pen environments with insufficient lighting, Buayai et al applied adaptive thresholding using an integral image for segmentation and used adaptive partitioning with connected components [45]. This step was followed by using the maximum entropy threshold of each partition, and merging the results from both steps, creating a more robust segmentation procedure.…”

WEED DETECTION IN WHEAT CROP USING UAV for PRECISION AGRICULTURE

“…Furthermore, this may lead to monitor and control of weed infections, crop illness, crop row alignment and weed patches detection etc . Thence by introducing the weed robotics and artificial intelligence in the area of agriculture for different problems provide massive benefits toward easiness in farming and crop yield enhancement (Buayaui et al, 2017). Although, there are still many challenges those must be solved to provide fully automated solution for precision agriculture industry (Chen et al, 2018).…”

“…Despite to this, there are several other factors those provide robust evidence to use adopt UAV for precision agriculture issues. It requires some simple steps for one-time flight of UAV for data acquisition process (Buayaui et al, 2017). In addition to benefits of UAV discussed above, all types of vegetation indices can be accurately applied on UAV based imagery and suitable results can be obtained once the vegetation fraction is successfully separated from images.…”

Depth-Based Detection of Standing-Pigs in Moving Noise Environments

“…presented by commercial farms. Table 1 summarizes the topview-based pig monitoring results introduced recently [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. Two-dimensional gray-scale or color information has been used to detect a single pig in a pen or a specially built facility (i.e., in “constrained” environments) [ 9 , 10 , 11 ].…”

“…Two-dimensional gray-scale or color information has been used to detect a single pig in a pen or a specially built facility (i.e., in “constrained” environments) [ 9 , 10 , 11 ]. However, even with advanced techniques applied to 2D gray-scale or color information, it remains challenging to detect multiple pigs accurately in a “commercial” farm environment [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. For example, images from a gray-scale or RGB camera are affected by various illuminations in a pig pen.…”