Shuqing Han

Review of automatic detection of pig behaviours by using image analysis

Automatic detection of lying, moving, feeding, drinking, and aggressive behaviours of pigs by means of image analysis can save observation input by staff. It would help staff make early detection of diseases or injuries of pigs during breeding and improve management efficiency of swine industry. This study describes the progress of pig behaviour detection based on image analysis and advancement in image segmentation of pig body, segmentation of pig adhesion and extraction of pig behaviour characteristic parameters. Challenges for achieving automatic detection of pig behaviours were summarized.

Estimation of Serum Vitamin A Level by Color Change of Pupil in Japanese Black Cattle

Abstract Color change of pupil area was investigated in Red, Green, Blue (RGB) and Hue, Saturation, Intensity (HSI) color models from July to November in 2010 and from May to December in 2011 to estimate the serum vitamin A level of Japanese black cattle during their vitamin A controlled stage. A 2CCD camera was used to acquire the eye images. The results showed lower vitamin A level cattle usually accompanied with higher red component value, lower saturation in their eye images. An estimation model was built based on red and green component ratio. The estimation error is about 10 IU/dL. The reasonable result shows the feasibility to estimate the vitamin A level by color change of pupil area in Japanese black cattle.

Effects of Low Serum Vitamin A Level on Pupillary Light Reflex in Japanese Black Cattle

Abstract Four parameters of pupillary light reflex were investigated from May to December in 2011 to study the effects of low serum vitamin A level on pupillary light reflex in Japanese black cattle during their vitamin A controlled stage. A 2CCD camera was used to acquire the pupillary light reflex images. Constriction amplitude in 1 second, maximum constriction velocity, maximum velocity time and initial pupil roundness were used for pupillary light reflex analysis. Cattle in low vitamin A level period had larger constriction amplitude in 1 second ( p =0.001), higher maximum constriction velocity ( p =0.016) and thinner resting pupil ( p =0.003). This result showed pupillary light reflex analysis can be used as indicators for cattle management during vitamin A controlled stage.

Machine vision based prediction of serum vitamin A level in Japanese Black Cattle by pupillary light reflex analysis

To increase the BMS (Beef Marbling Standard) score of Japanese Black Cattle, keeping the cattle serum vitamin A at a low level (30–40 IU/dl) during fattening age is an effective way. The traditional method of monitoring the serum vitamin A level is blood assay. However, it is costly, time-consuming and makes cattle stressful. A new approach by using 2CCD camera is proposed in this study. Pupil reflex of cattle with different vitamin A level was analyzed by image processing.

Application Progress of Agricultural Internet of Things in Major Countries

Agriculture is the important application field of the agricultural Internet of Things. With the development of information technology and computer network technology, IoT (Internet of Things) has entered various fields such as agricultural production, management, management, service and so on. On the basis of systematic discussion of the application of Internet of Things in the developed countries such as USA, Japan and Holland, the paper looks forwards to the key application fields in the future such as agricultural resources and environment monitoring, precision operation in the field, intelligent monitoring of facility gardening, fine management of livestock and poultry, prevention and control of diseases and pests, intelligent irrigation, agricultural product quality trace ability, etc.

Robust Image Segmentation Method for Cotton Leaf Under Natural Conditions Based on Immune Algorithm and PCNN Algorithm

In the actual cotton planting environment, rapid change of light within a day, reflection from different backgrounds and different weather conditions can affect the imaging of cotton. Therefore, the crop object segmentation is difficult. Images which were captured in 12 natural scenes during cotton planting, including three weather conditions, such as sunny, cloudy and rainy and four soil cover conditions, such as white mulch film, black mulch film, straw and bare soil were regarded as the research objects. This paper presents the cotton leaf segmentation method based on Immune algorithm and pulse coupled neural networks (PCNN). First, 17 color components of white mulch film, black mulch film, straw, bare soil and cotton under the conditions of sunny, cloudy and rainy days were analyzed by using statistical method. Three high feasible and anti-light color components were selected by histogram statistical with mean gray value. Second, the optimal parameters of PCNN model and the optimal number of iteration...

Application of Curve Fitting and Wavelength Selection Methods for Determination of Chlorogenic Acid Concentration in Coffee Aqueous Solution by Vis/NIR Spectroscopy

Coffee is considered as a functional food due to its being rich in bioactive compounds, mainly chlorogenic acid (CGA). CGA concentration in coffee aqueous solution was investigated based on visible/near-infrared (Vis/NIR) spectroscopy in this research. To enhance the spectral difference among different samples and increase the signal to noise ratio, Lorentz function curve fitting was applied to fit raw Vis/NIR spectra of samples. Then, the fitting parameters were used to correct raw full spectra. Partial least squares (PLS) regression method was used to develop calibration models of CGA concentration. Full-spectrum models were built with raw and fitting parameter-corrected spectra, respectively. Further, wavelength selection methods, such as genetic algorithms (GAs) and success projection algorithms (SPAs), were applied to eliminate redundancy information and identify relevant information from full spectra. Calibration models based on the effective wavelengths selected by GA and SPA methods were developed. The overall results showed that LFPs a/b-corrected spectra had a better performance compared with other processing methods. Performance of the selected wavelength model was better than that of the full-spectrum model. Final results indicated that the SPAs-PLS method provided a more precise prediction model of CGA concentration with Rc of 0.913 and Rcv of 0.795.

An Analysis on Innovative Research Fronts for the Information-based Agriculture in China

Against the historical backdrop of synchronizing industrialization, informationization, urbanization and agricultural modernization in China, it is of a material immediate significance to clearly define research priorities and development orientations for the future in the field of information-based agriculture. This article, through summarizing the status quo and the latest developments in the field of China’s research into the information-based agriculture, specifically proposes certain scientific problems and key technologies for future research in the area of information-based agriculture, and gives a systematic analysis on major items for research in the area of China’s information-based agriculture for the next 5 to 10 years.