Agricultural extension workers play an essential role in the productivity of agricultural systems. Based on the actual conditions in the field, it can be seen that the level of extension services still needs to be higher due to a lack of human resources in the field of extension services. This research was conducted to determine the effectiveness of agricultural extension services andthe factors that influence the effectiveness of agricultural extension to farmers in Baubau City, Indonesia. The analytical method includes exogenous latent variables: human resources, technological progress, farming capital, farmer age, education, and farming experience. The effectiveness of agricultural extension is used as an endogenous latent variable. The research sample consisted of 110 rice farmers in Baubau City, and the Slovin formula was used to calculate the sample. The data collection for this research was carried out by distributing questionnaires to respondents, in-depth interviews, and direct observation in the city of Baubau. Using the AMOS application, quantitative analysis was carried out through structural equation modeling (SEM). The study results show that: (1) The factors that influence the effectiveness of agricultural extension in Baubau City are farming capital, farmer age, education, farming experience, and human resources, and (2) the influence of these factors on the effectiveness of agricultural extension is as follows: if the farming capital is high, human resources can be increased. In addition, the higher the farmer’s age, the lower the need for human resources. It is also noted that higher farmer education contributes to increased human capital, and increased experience in farming is associated with increased human capital. Thus, an increase in human resources will increase the effectiveness of agricultural extension. Significant factors that influence the effectiveness of agricultural extension in Baubau City, Southeast Sulawesi, are farming capital, farmer age, education, farming experience, and human resources.
Fundamental sheep behaviours, for instance, walking, standing, and lying, can be closely associated with their physiological health. However, monitoring sheep in grazing land is complex as limited range, varied weather, and diverse outdoor lighting conditions, with the need to accurately recognise sheep behaviour in free range situations, are critical problems that must be addressed. This study proposes an enhanced sheep behaviour recognition algorithm based on the You Only Look Once Version 5 (YOLOV5) model. The algorithm investigates the effect of different shooting methodologies on sheep behaviour recognition and the model’s generalisation ability under different environmental conditions and, at the same time, provides an overview of the design for the real-time recognition system. The initial stage of the research involves the construction of sheep behaviour datasets using two shooting methods. Subsequently, the YOLOV5 model was executed, resulting in better performance on the corresponding datasets, with an average accuracy of over 90% for the three classifications. Next, cross-validation was employed to verify the model’s generalisation ability, and the results indicated the handheld camera-trained model had better generalisation ability. Furthermore, the enhanced YOLOV5 model with the addition of an attention mechanism module before feature extraction results displayed a mAP@0.5 of 91.8% which represented an increase of 1.7%. Lastly, a cloud-based structure was proposed with the Real-Time Messaging Protocol (RTMP) to push the video stream for real-time behaviour recognition to apply the model in a practical situation. Conclusively, this study proposes an improved YOLOV5 algorithm for sheep behaviour recognition in pasture scenarios. The model can effectively detect sheep’s daily behaviour for precision livestock management, promoting modern husbandry development.
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