On-Line Automated Inspection of Poultry Carcasses by Machine Vision
Development of an automated poultry inspection system that is low-cost, operates 
with minimum human intervention, and is able to maintain its accuracy is important to 
the U.S. Food Safety and Inspection Service (FSIS) and the poultry industry. Such 
a system, placed strategically in the processing plants, would help improve the 
inspection speed, minimize problems of human error and variability, improve the 
effectiveness of the federal inspection program, and increase the slaughter plants’ 
productivity. This paper describes an automated poultry inspection system that 
utilizes machine vision technology. The system was tested on-line at a poultry plant. 
The results showed that the classification accuracy for wholesome and unwholesome 
carcasses were 94 and 87%, respectively.
DESIGN OF A DUAL-CAMERA SYSTEM FOR POULTRY CARCASSES INSPECTION
Two dual-camera systems were developed for on-line inspection of poultry carcasses: one to image the front 
of the bird and the other to image the back. Each system consists of two identical black and white cameras equipped with 
interference filters of 540 nm and 700 nm. Both cameras capture spectral images simultaneously. Object-oriented analysis 
was performed to identify the attributes of individual software components and the relationships among these software 
components. These individual software components were then organized by the object patterns to form a software 
architectural framework for on-line image capture, off-line development of classification models, and on-line 
classification of carcasses into wholesome and unwholesome categories. Model development and testing was performed 
on 331 chickens independently classified by a veterinarian. For off-line model development, the accuracies for 
differentiating between wholesome and unwholesome carcasses were 96.2% and 88.5% at 540 nm and 700 nm, 
respectively, for the front images and 95.7% and 85.1% at 540 nm and 700 nm, respectively, for the back images. On-line 
classification for 128 new samples combined the filter information within each system, using selected neural network 
models. The front imaging system gave accuracies of 91%, 98% and 95% for normal, abnormal and combined carcasses, 
respectively. The back imaging system gave 84%, 100% and 92%.
Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation
In this paper, we present a technique to estimate citrus fruit yield from the tree images. Manually counting the fruit for yield estimation for marketing and other managerial tasks is time consuming and requires human resources, which do not always come cheap. Different approaches have been used for the said purpose, yet separation of fruit from its background poses challenges, and renders the exercise inaccurate. In this paper, we use k-means segmentation for recognition of fruit, which segments the image accurately thus enabling more accurate yield estimation. We created a dataset containing 83 tree images with 4001 citrus fruits from three different fields. We are able to detect the on-tree fruits with an accuracy of 91.3%. In addition, we find a strong correlation between the manual and the automated fruit count by getting coefficients of determination R2 up to 0.99.