Machine Vision System for Determining Citrus Count and Size on a Canopy Shake and Catch Harvester
A machine vision-based citrus fruit counting system was developed for a continuous canopy shake and catch harvester. The system consisted of a 3CCD camera, four halogen lamps, an encoder, and a laptop computer. A total of 719 images were taken during an experiment on a test bench at the Citrus Research and Education Center (Lake Alfred, Fla.) and were used for analysis. The system was also tested on a canopy shake and catch harvester at a grove located in Fort Basinger, Florida, where a total of 773 images were acquired and 60 images were used for validation. Fruit weight was measured during image acquisition in 14 test bench experiments and in two field tests with a commercial canopy shake and catch harvester. An image processing algorithm that could identify fruit and measure its size was developed using a Bayesian classifier, morphological operations, and watershed segmentation. From the sets of color images, the number of fruit and total fruit areas were measured using the developed algorithm. Finally, the number of citrus fruits that were identified by the image processing algorithm during the test bench experiment was compared against actual fruit weight. The coefficient of determination (R2) between them was 0.962. To validate the canopy shake and catch harvester experiment, the number of fruit was counted manually from a total of 60 images. Density clustering was used to enhance the result of the Bayesian classifier. The manual count was compared with the image processing algorithm count. The R2 value was 0.891 between the actual and estimated number of fruit.
Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove
A recognition and counting algorithm for immature green citrus was presented using regular colour images.An adaptive red (R) and blue (B) chromatic map (ARB) was proposed.A block-matching method, the sum of absolute transform difference, was used to detect potential fruit pixels.Same learning sets were used to train classifier for different classification stages.The proposed method yielded a correct detection accuracy of 83%. Recognition and detection of green immature citrus fruit more accurately and efficiently in groves under natural illumination conditions provides a promising benefit for growers to plan application of nutrients during the fruit maturing stages and estimate their yield and profit prior to the harvesting period. The goal of this study was to develop a robust and fast algorithm to detect and count immature green citrus fruit in individual trees from colour images acquired with different fruit sizes and under various illumination conditions. Adaptive Red and Blue chromatic map (ARB) was created and combined with the Hue image extracted after histogram equalization (HEH). The sum of absolute transformed difference (SATD), a block-matching method, was applied to detect potential fruit pixels. After OR operation of the results obtained from colour and SATD analysis which kept as many fruit pixels as possible, a kernel support vector machine (SVM) classifier was built with same learning sets used for different classification stages to remove false positives based on five selected texture features. The algorithm was evaluated with a set of testing images, and achieved more than 83% recognition accuracy. The proposed method can provide a more efficient way for green citrus identification in a grove using colour images.
Drug Testing in Public Agencies: Are Personnel Directors Doing Things Right?
The results of a survey of 300 public agencies personnel directors are reported. The report describes the drug and alcohol testing policies and procedures now used by these respondents. The implications of the survey results are evaluated in terms of critical issues of personnel management practice and public policy: (1) the protection of employees' constitutional rights, (2) the public's right to an effective public service, and (3) management's need to reduce liability risks and to increase program effectiveness.