Computer Vision System For Measurement Research Articles

Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms

This study entailed the design and implementation of a computer vision system for cow individual feed intake measurement, based on deep Convolutional Neural Networks (CNNs) models, and a low-cost RGB-D (Red, Green, Blue, Depth) camera. Individual feed intake of dairy cows is an important variable currently unavailable in commercial dairies. An RGB-D camera was positioned above the feeding area in an open cowshed. Feed intake was estimated by combining information from the RGB and depth images. Cow identification was conducted using the RGB image. Deep learning algorithms for identification and intake estimation were developed using CNN models. Data for CNN training were acquired by a specially developed automatic data acquisition system. A range of feed weights under varied configurations were collected over a period of seven days with the setup, which included an automatic scale, cameras, and a micro-controller. Test data for feed intake was acquired in an open cowshed research dairy farm, wherein the cows were fed Total Mix Ration (TMR). Images of cows eating over a period of 36 h provided the test data for cow identification. The system was able to accurately identify 93.65% of the cows. The amount of feed consumed, which ranged from 0 to 8 kg per meal, was measured with mean absolute and square errors (MAE and MSE) of 0.127 kg, and 0.034kg2 respectively. The analysis showed that the amount and diversity of data are important for model training. Better results were achieved for the model that was trained with high-diversity data than the model trained with homogeneous data (MAE of 1.025 kg, and MSE of 2.845 kg2 for a model trained on shadow conditions only). Additionally, the training analysis shows that the model based on RGB-D data shows better results than the model based on depth channel data without RGB (MAE of 0.241 kg, and MSE of 0.106 kg2). These results suggest the potential of low-cost cameras for individual feed intake measurements in advanced dairy farms.

Milled industrial beet color kinetics and total soluble solid contents by image analysis

Industrial beets are an emerging feedstock for biofuel and bioproducts industry in the US. Milling of industrial beets is the primary step in front end processing for ethanol production. Milled beets undergo color change during juice extraction. A custom designed computer vision system for measurement of milled beet color kinetics, consisting of a digital camera, custom-designed non-reflective enclosure, and color calibration method was developed in the present study. Beet samples of five different total soluble solids (TSS) contents were prepared by washing with cold water for color kinetics measurement. An artificial neural network model was used for converting the red, green, and blue (RGB) values of the acquired sample images to L*a*b* values. Seven color parameters (L*, a*, b*, hue, chroma, browning index (BI), and total color change (ΔE)) were analyzed. Page, user-defined polynomial, and fractional conversion models gave better fits for the experimental data with color parameters than the zeroth order, first order, exponential, and Peleg kinetic models. Of the color parameters studied, L* (Page R2>0.99, user-defined polynomial R2>0.97, and fractional R2>0.93) and ΔE (Page R2>0.98, user-defined polynomial R2>0.94, and fractional R2>0.85) gave the best description for the color change kinetics of milled beets. Developed TSS prediction models from the color measurements based on Page model constant, kp with color parameter, L* gave good prediction (R2=0.99), also did the simple linear model based on direct color values (R2=0.98). Measurement and mathematical modeling of milled industrial beets color kinetics will serve as important quality assessment tool in processing the beets for various renewable fuel and products applications.