Yingjun Xiong

An automatic splitting method for the adhesive piglets’ gray scale image based on the ellipse shape feature

Abstract The average weight of piglets in lactation can be monitored automatically by piglets’ average weight monitoring systems which are designed based on wireless multimedia sensor networks. Piglets counting in an automatic manner for piglets images is the foundation of these systems. Adhesive piglets may exist in an image due to the social character of piglets, which challenges the image splitting and automatic piglets counting. This paper proposes a segmentation algorithm for adhesive piglets images based on ellipse fitting method. Firstly, ellipse fitting is implemented for a large number of images which have one piglet. Parameters range of ellipses fitted by images with a single piglet on different age is extracted. Secondly, contours of connected components in an adhesive piglets image are extracted. Each contour is segmented based on concave points. Ellipse fitting is implemented for each contour segment. Finally, 5 rules for ellipse merging are put forwarded, which are used to merge anomalous ellipses. After ellipse merging, the number of ellipses equals the number of piglets. The proposed algorithm is applied to adhesive piglets images in Matlab R2012b and the experimental results show that the counting accuracy exceeds 86% when the number of piglets is less than 7. The algorithm provides the foundation for the piglets’ average weight monitoring systems.

Design of a Wireless Sensor Network for Farmland Monitoring

Farmland monitoring is of great significance in precision agriculture. In this paper, we design a wireless sensor network to monitor farmland information, including air temperature and humidity, light intensity, soil moisture, soil PH value, and the growth of crops. We carry out an experiment in the cornfield of Hongzehu farm. It shows that the monitoring system is effective.

Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method

Abstract Crop row detection is critical for precision agriculture and automatic navigation. In this paper, a novel automatic and robust crop row detection method is proposed for maize fields based on images acquired from a vision system. As the image quality is easily affected by weed pressure and gaps in the crop rows, the proposed method was designed with the required robustness in order to deal with these undesirable conditions, and it consists of three sequentially linked phases: image segmentation, feature points extraction, and crop row detection. The image segmentation is based on the application of a modified vegetation index and double thresholding combining the Otsu method with the particle swarm optimization, thus achieving a separation between the weeds and crops. During the procedure of crop row detection, the position clustering algorithm and shortest path method were applied successively to confirm the final clustered feature point set. Finally, a linear regression method based on least squares was employed to fit the crop rows. The experimental results show that the detection accuracy of this proposed method is 0.5°, which out-performs the classical approach based on the Hough transform.

Determination of soluble solid content in multi-origin ‘Fuji’ apples by using FT-NIR spectroscopy and an origin discriminant strategy

Abstract Apple is widely planted all over the world. Origin variability influences the internal quality of apples because soil characteristics, light effects, nutrition, weather conditions, as well as growing management vary from orchard to orchard. However, if taking spectral variations caused by the origin variability of apple samples from different orchards into account, the fruit quality parameters could not be measured or predicted with high accuracy by using the current models without updates. To improve the practicability and accuracy of the prediction models, a multi-origin regression model for the determination of soluble solids content in apples from three origins by using FT-NIR spectroscopy and a model search strategy was developed in this paper. In this model, based on the wavelengths selected by competitive adaptive reweighted sampling algorithm (CARS), partial least squares discriminant analysis (PLS-DA) was trained and applied to identify the geographical origins of the apple samples. The results indicate that the samples spectra were correctly matched to the corresponding classes and a 98.1% correct classification was achieved. Partial least squares regression (PLS) was used to establish three single-origin calibration models for the determination of soluble solids content (SSC) in apples from three different origins, and meanwhile, CARS algorithm was also applied to select the most effective wavelengths for calibration models. Then, the multi-origin CARS-PLS model for determination of SSC in apples from three origins was developed combined with origin discriminant and the proposed model search strategy. It was concluded that the multi-origin CARS-PLS model achieved more satisfying results than the single-origin CARS-PLS models for the determination of SSC, with R p and RMSEP values for the apple samples from three geographical origins being 0.921, 0.759, 0.924 and 0.661, 0.673, 0.547 Â ° Brix, respectively. The above results indicate that it is promising to build a multi-origin CARS-PLS model to predict SSC for apples based on an origin discriminant approach to reduce the effect of geographical origin.