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In Kenya, threshing of common beans is mainly by traditional method using sticks and animal tramping, which are slow, inefficient and tedious. Consequently, there is a need to develop portable threshers locally available in the market for small and medium-scale farmers. The objective of this study was to simulate design variables effect on the performance of a common beans portable thresher. Sizing of design variables and parameters was key in development of bean thresher. This could be achieved by costly experiments or use of prediction mathematical model equation. The later method was used by developing mathematical models from combination of Buckingham pi theorem and reference to other similar work in literature. The predicting equation for power requirement, grain losses, grain damages, efficiency and throughput capacity were developed and validated using experimental thresher from the same study. The results showed that there was a positive correlation with R2 of 0.9. Based on actual data and 10% absolute residual error interval, the prediction performance of the developed models were above 77%. The results noted that increase in cylinder peripheral speed of the pegs resulted into increase in power requirement, bean grains damages, threshing efficiency and throughput capacity. Also increase in effective cylinder diameter caused increase in threshing efficiency and grain damages.

Monitoring of poultry welfare-related bio-processes and bio-responses is vital in welfare assessment and management of welfare-related factors. With the current development in information technologies, computer vision has become a promising tool in the real-time automation of poultry monitoring systems due to its non-intrusive and non-invasive properties, and its ability to present a wide range of information. Hence, it can be applied to monitor several bio-processes and bio-responses. This review summarizes the current advances in poultry monitoring techniques based on computer vision systems, i.e., conventional machine learning-based and deep learning-based systems. A detailed presentation on the machine learning-based system was presented, i.e., pre-processing, segmentation, feature extraction, feature selection, and dimension reduction, and modeling. Similarly, deep learning approaches in poultry monitoring were also presented. Lastly, the challenges and possible solutions presented by researches in poultry monitoring, such as variable illumination conditions, occlusion problems, and lack of augmented and labeled poultry datasets, were discussed.