Rapid advancements in Cyber-Physical System (CPS) capabilities have motivated farmers to deploy this ecosystem on their farms. However, there is a growing concern among users regarding the security risks associated with CPS. Especially with rising number of cyber-attacks on CPS, such as modifying sensor readings, interrupting operations, etc. Therefore, this paper describes a security surveillance framework to detect deviations in the ecosystem by incorporating a digital twin supported anomaly detection model. The reason for incorporating digital twins is that they add value by enabling real-time monitoring of connected smart farms. We pre-process the collected data from sensors deployed on the smart farm setup. The pre-processed data is fused with our smart farm ontology to populate a knowledge graph. The generated graph is further queried to extract the necessary sensor data. We utilize the extracted normal data to train the anomaly detection model. Further, we tested our model if it identifies abnormal values from sensors by simulating anomalous use case scenarios specific to our ecosystem.

In recent years, machine learning approaches are gaining popularity with the advent of big data. The massive amount of data generated, when served as an input to machine learning approaches, provides useful insights. Adoption of these approaches in the agricultural sector has immense potential to increase crop productivity and quality. In this paper, we analyze the crop data collected from an agriculture site in Rajasthan, India, that includes both Rabi and Kharif cropping patterns. In addition, we utilize a smart farm ontology that contains concepts and properties related to the agricultural domain. We link the collected data and our smart farm ontology to populate a knowledge graph. We utilize the generated knowledge graph to provide structural information and aggregate data by using SPARQL queries. The aggregated data is further used by our machine learning models to predict the crop yield to benefit farmers and various stakeholders. We also analyze and compare our results obtained for various machine learning models used.

Providing climate data records to infer seasonal and interannual variations from multiple heterogeneous sources is a challenging data fusion. We combine the Compressive Sensing (CS) and Deep Learning (DL) into a single framework for fusing data from multiple sensors to improve spatial and temporal resolution for long term analysis. CS is used as an initial guess to combine data from multiple sources. DL models, using Long Short-Term Memory Neural Network (LSTM/RNN), Convolutional Neural Network (CNN), refine and further improve the data fusion from CS algorithm's output. The proposed framework has been tested the using daily global observations from two satellites the NASA Orbiting Carbon Observatory-2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT). Our framework achieves lower errors and high correlation compared with the original data. The quality of fused data is evaluated by comparing again AmeriFlux ground station's datasets. Long term trends using fused data over the United States indicate an increase of 8 parts per million (ppm) annually in XCO2 over four years with Root Mean Square Errors of 0.39 ppm and correlation of 0.98 compared with original data. Interannual variability of the seasonal cycle shows an increase in years 2015 - 2017, but a sharp decrease in 2018.