The coverage and detection limit of a sampling point for robotics radiation mapping.

The sensor coverage problem aims to maximize the coverage of a target area with a fixed or minimum number of sensors. However, the sampling point coverage for radiation mapping has yet to be specified or adequately established. When dealing with unknown radiation fields, it is critical that the placements of sampling points will ensure that all hotspots are detected and accurately identified. Therefore, the concept of coverage and detection limit for a sampling point in radiation mapping is proposed in this paper. The proposed concept relates the angular dependency of the radiation measurement instruments with the detector detection limit or minimum detectable amount (MDA). To demonstrate the implementation, the concept is used to compute the sensitivity of the radiation map for coverage radiation mapping with mobile robot. Simulation results showed that hotspots with intensity equal to or above the sampling point detection limit were successfully detected regardless of their position within the coverage circle. Moreover, the experimental results of coverage radiation mapping showed that the concept can be used to compute the resolution of the radiation map. This will help the user to efficiently configure the appropriate grid size that suit their mapping situation and requirements.

Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models.

Bacteria- or virus-infected chicken is conventionally detected by manual observation and confirmed by a laboratory test, which may lead to late detection, significant economic loss, and threaten human health. This paper reports on the development of an innovative technique to detect bacteria- or virus-infected chickens based on the optical chromaticity of the chicken comb. The chromaticity of the infected and healthy chicken comb was extracted and analyzed with International Commission on Illumination (CIE) XYZ color space. Logistic Regression, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Decision Trees have been developed to detect infected chickens using the chromaticity data. Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chicken's comb converged from red to green and yellow to blue. The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95% accuracy, followed by SVM-RBF kernel, and KNN with 93% accuracy, Decision Tree with 90% accuracy, and lastly, SVM-Sigmoidal kernel with 83% accuracy. The iteration of the probability threshold parameter for Logistic Regression models has shown that the model can detect all infected chickens with 100% sensitivity and 95% accuracy at the probability threshold of 0.54. These works have shown that, despite using only the optical chromaticity of the chicken comb as the input data, the developed models (95% accuracy) have performed exceptionally well, compared to other reported results (99.469% accuracy) which utilize more sophisticated input data such as morphological and mobility features. This work has demonstrated a new feature for bacteria- or virus-infected chicken detection and contributes to the development of modern technology in agriculture applications.