尽管环境条件不利，现代农业仍在不断发展以增加产量。一种有希望的方法是“温室种植”，为耕种的植物提供了微气候，以克服不利的气候。然而，大型的温室在整个复合物中都会形成不均匀的微气候，需要高度的人类监督。我们建议部署农业机器人，以在温室中创造和维持积极的生态条件，从而降低人工成本并增加产量。该原型将包含两个主要系统，即导航系统和数据分析系统。导航系统将由Arduino控制，并且将使用ESP8266微芯片处理数据分析。用于测量温室参数的许多传感器将安装在机器人上。它将遵循预定义的路径，同时在检查点进行读数。微芯片将从传感器收集和处理数据，传输到云，并向执行器发出命令。将定期测量土壤和气候参数，例如温度，湿度，光强度，土壤水分，pH值。当参数不在指定的范围内时，农业机器人将采取纠正措施，例如打击器/加热器，开始灌溉等。如果需要外部干预，例如肥料，则将相应地指示。在大规模的温室中部署这样的农业机器人来监测和控制小气候，可以降低人工成本，同时提高生产率。尽管有初始成本，但它可以通过提供灵活性，低功耗和易于管理来提供高度投资回报，以帮助温室提高水效率，提供均匀分散和受控的阳光强度，温度和湿度。

In the current times, the fear and danger of COVID-19 virus still stands large. Manual monitoring of social distancing norms is impractical with a large population moving about and with insufficient task force and resources to administer them. There is a need for a lightweight, robust and 24X7 video-monitoring system that automates this process. This paper proposes a comprehensive and effective solution to perform person detection, social distancing violation detection, face detection and face mask classification using object detection, clustering and Convolution Neural Network (CNN) based binary classifier. For this, YOLOv3, Density-based spatial clustering of applications with noise (DBSCAN), Dual Shot Face Detector (DSFD) and MobileNetV2 based binary classifier have been employed on surveillance video datasets. This paper also provides a comparative study of different face detection and face mask classification models. Finally, a video dataset labelling method is proposed along with the labelled video dataset to compensate for the lack of dataset in the community and is used for evaluation of the system. The system performance is evaluated in terms of accuracy, F1 score as well as the prediction time, which has to be low for practical applicability. The system performs with an accuracy of 91.2% and F1 score of 90.79% on the labelled video dataset and has an average prediction time of 7.12 seconds for 78 frames of a video.