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This study investigates the adoption challenges of Farmonaut’s Management Information System (MIS), a satellite-based smart agriculture solution, within the Bo‘ka and Chinoz districts of Uzbekistan. Using the Technology-Organization-Environment (TOE) framework, the research analyzes how infrastructural limitations, institutional dynamics, and environmental constraints influence the implementation of digital agricultural technologies. Employing a qualitative case study approach grounded in secondary data, the findings reveal key technological barriers such as limited internet access, low digital literacy, and lack of system localization. Organizational challenges include inadequate managerial support, weak training programs, and poor inter-agency coordination. Environmentally, the efficacy of MIS tools is hindered by climate variability, land degradation, and underdeveloped irrigation infrastructure. Despite these limitations, the study identifies areas of potential, particularly when supported by localized training, policy incentives, and public-private partnerships. The results offer policy-relevant insights into how smart farming technologies can be tailored to fit the specific socio-economic and environmental contexts of developing countries.

Considering the trends in the market, innovations and technology-based developments in Pakistani banks, it is essential to know what impacts on employee performance in the banking sector. Hence, this paper suggests three factors: corporate social responsibility, public service motivation, and digital competencies and examines how they influence employee performance. It also investigates whether psychological empowerment can intervene. This paper concentrates on Pakistani banks such as MCB, HBL, UBL, NBP, Bank Alfalah and Meezan Bank. The online questionnaire was designed to collect data from 300 employees of the given banks. The collected data was analysed using structural equation modelling (SEM) technique. The results revealed that all the factors positively impacted psychological empowerment and employee performance. Similarly, psychological empowerment positively impacted employee performance as well as played a positive mediating role between corporate social responsibility, public service motivation, digital competencies and employee performance. The implications of this study are not just limited to the Pakistani banking sector, but are also global, for organizations in general, in terms of how employee performance can contribute to an organization’s growth within this competitive environment.

Satisfaction and attitude are two important psychological traits of human beings. Satisfaction refers to the emotional gratification that comes from accomplishing one's professional or personal goals, while attitude is the inclination to react favorably or unfavorably to a specific thing, person, or circumstance. Literature suggests that job satisfaction and attitude have a bilateral relationship, with both influencing each other. Without a positive attitude, no one can perform well in their profession. In this context, the purpose of this study is to identify the relationship between job satisfaction and attitude. This study employed a quantitative design. A survey method was used to collect the required data, utilizing an attitude scale and a job satisfaction scale as data collection tools. Two independent samples, one from Nepal and the other from India, each consisting of 75 schoolteachers selected through random sampling, were used to triangulate the results. Data was analyzed using SPSS. In Nepal, job satisfaction and attitude were found to be significantly correlated at the 0.01 level of significance, with a correlation coefficient of .490. For the Indian sample, the correlation coefficient was .681, and the analysis showed a similarly significant correlation at the 0.01 level of significance. The correlation coefficient is slightly higher in the Indian sample than in the Nepali sample, but both results indicate a similar relationship between job satisfaction and attitude. This correlational analysis indicates that a person's attitude is directly dependent on their level of job satisfaction. To improve employees' attitudes toward their profession, concerned authorities should enhance job satisfaction by reviewing and improving the facilities provided to them.

This study presents TomatoBot, a semi-autonomous agricultural robot designed for tomato detection, navigation, and harvesting. The system integrates computer vision, semantic understanding, and robotic manipulation to address labor-intensive tomato harvesting tasks. TomatoBot detects and classifies tomatoes into six categories using a custom-trained YOLOv8 model and performs navigation by identifying crop lanes and following a computed centerline. Environmental understanding is achieved through semantic segmentation of soil, crop, and background classes using a U-Net with FCN and a ResNet50 backbone. Lane centerlines are estimated using a Deep Hough Transformer with a MobileNetV2 backbone, where geometric interpolation is applied to generate a stable navigation path. The robot is controlled by a Raspberry Pi 4 and equipped with a 6-DOF robotic arm driven by inverse kinematics for tomato plucking. A mobile application enables real-time monitoring and semi-manual interaction. Experimental results demonstrate a mean average precision (mAP@50) of 88.1% for tomato detection, an overall pixel accuracy of 96.11% for semantic segmentation, and an F-measure of 90.45% for semantic line detection, resulting in improved navigation stability. Overall, TomatoBot demonstrates the feasibility of combining lightweight AI models with robotic manipulation for precision farming and provides a scalable foundation for future autonomous agricultural systems.