Accurate projections of seasonal agricultural output are essential for improving food security. However, the collection of agricultural information through seasonal agricultural surveys is often not timely enough to inform public and private stakeholders about crop status during the growing season. Acquiring timely and accurate crop estimates can be particularly challenging in countries with predominately smallholder farms because of the large number of small plots, intense intercropping, and high diversity of crop types. In this study, we used RGB images collected from unmanned aerial vehicles (UAVs) flown in Rwanda to develop a deep learning algorithm for identifying crop types, specifically bananas, maize, and legumes, which are key strategic food crops in Rwandan agriculture. The model leverages advances in deep convolutional neural networks and transfer learning, employing the VGG16 architecture and the publicly accessible ImageNet dataset for pretraining. The developed model performs with an overall test set F1 of 0.86, with individual classes ranging from 0.49 (legumes) to 0.96 (bananas). Our findings suggest that although certain staple crops such as bananas and maize can be classified at this scale with high accuracy, crops involved in intercropping (legumes) can be difficult to identify consistently. We discuss the potential use cases for the developed model and recommend directions for future research in this area.

Timely and accurate agricultural information is needed to inform resource allocation and sustainable practices to improve food security in the developing world. Obtaining this information through traditional surveys is time consuming and labor intensive, making it difficult to collect data at the frequency and resolution needed to accurately estimate the planted areas of key crops and their distribution during the growing season. Remote sensing technologies can be leveraged to provide consistent, cost-effective, and spatially disaggregated data at high temporal frequency. In this study, we used imagery acquired from unmanned aerial vehicles to create a high-fidelity ground-truth dataset that included examples of large mono-cropped fields, small intercropped fields, and natural vegetation. The imagery was acquired in three rounds of flights at six sites in different agro-ecological zones to capture growing conditions. This dataset was used to train and test a random forest model that was implemented in Google Earth Engine for classifying cropped land using freely available Sentinel-1 and -2 data. This model achieved an overall accuracy of 83%, and a 91% accuracy for maize specifically. The model results were compared with Rwanda’s Seasonal Agricultural Survey, which highlighted biases in the dataset including a lack of examples of mixed land cover.

Demonstration of an electrochemical toilet wastewater treatment and disinfection technology at the scale of an apartment building and translation of the system into a commercial product.

BackgroundStress experienced by law enforcement officers is often extreme and is in many ways unique among professions. Although past research on officer stress is informative, it is limited, and most studies measure stress using self-report questionnaires or observational studies that have limited generalizability. We know of no research studies that have attempted to track direct physiological stress responses in high fidelity, especially within an operational police setting. The outcome of this project will have an impact on both practitioners and policing researchers. To do so, we will establish a capacity to obtain complex, multisensor data; process complex datasets; and establish the methods needed to conduct idiopathic clinical trials on behavioral interventions in similar contexts.ObjectiveThe objective of this pilot study is to demonstrate the practicality and utility of wrist-worn biometric sensor-based research in a law enforcement agency.MethodsWe will use nonprobability convenience-based sampling to recruit 2-3 participants from the police department in Durham, North Carolina, USA.ResultsData collection was conducted in 2016. We will analyze data in early 2017 and disseminate our results via peer reviewed publications in late 2017.ConclusionsWe developed the Biometrics & Policing Demonstration project to provide a proof of concept on collecting biometric data in a law enforcement setting. This effort will enable us to (1) address the regulatory approvals needed to collect data, including human participant considerations, (2) demonstrate the ability to use biometric tracking technology in a policing setting, (3) link biometric data to law enforcement data, and (4) explore project results for law enforcement policy and training.