IoT-Based Monitoring System Applied to Aeroponics Greenhouse.

The inclusion of the Internet of Things (IoT) in greenhouses has become a fundamental tool for improving cultivation systems, offering information relevant to the greenhouse manager for decision making in search of optimum yield. This article presents a monitoring system applied to an aeroponic greenhouse based on an IoT architecture that provides user information on the status of the climatic variables and the appearance of the crop in addition to managing the irrigation timing and the frequency of visual inspection using an application developed for Android mobile devices called Aeroponics Monitor. The proposed IoT architecture consists of four layers: a device layer, fog layer, cloud layer and application layer. Once the information about the monitored variables is obtained by the sensors of the device layer, the fog layer processes it and transfers it to the Thingspeak and Firebase servers. In the cloud layer, Thingspeak analyzes the information from the variables monitored in the greenhouse through its IoT analytic tools to generate historical data and visualizations of their behavior, as well as an analysis of the system's operating status. Firebase, on the other hand, is used as a database to store the results of the processing of the images taken in the fog layer for the supervision of the leaves and roots. The results of the analysis of the information of the monitored variables and of the processing of the images are presented in the developed app, with the objective of visualizing the state of the crop and to know the function of the monitoring system in the event of a possible lack of electricity or a service line failure in the fog layer and to avoid the loss of information. With the information about the temperature of the plant leaf and the relative humidity inside the greenhouse, the vapor pressure deficit (VPD) in the cloud layer is calculated; the VPD values are available on the Thingspeak server and in the developed app. Additionally, an analysis of the VPD is presented that demonstrates a water deficiency from the transplanting of the seedling to the cultivation chamber. The IoT architecture presented in this paper represents a potential tool for the study of aeroponic farming systems through IoT-assisted monitoring.

Comparison of montage with conventional stereoscopic seven-field photographs for assessment of ETDRS diabetic retinopathy severity.

BACKGROUND: The ETDRS stereoscopic seven-field (7F) has been a standard imaging and grading protocol for assessment of diabetic retinopathy (DR) severity score in many clinical trials. To the best of our knowledge, the comparison between montage and stereoscopic 7F has not been reported in the literature. Therefore, the main purpose of this study is to compare agreement between montage and stereoscopic seven-field (7F) photographs in the assessment of DR severity. METHODS: Stereoscopic 7F photographs were captured from subjects with DR. Montages of monoscopic 7F images were created using Adobe Photoshop CS6 Extended©. The best quality image of each stereo pair was selected and placed on a 150 × 125-inch canvas field according to the standard location from field 1 to 7. All the fields were aligned following the vessels and overlaid using the built-in blending tool. The resulting montage was utilized for grading and compared with grading on stereoscopic 7F photographs. Three independent graders were asked to assess DR severity on stereoscopic 7F photographs and montage. Severity level agreement between stereo 7F and montage was cross-tabulated and the agreement of DR severity levels between stereoscopic 7-field images and montage was analyzed using κ intergrader agreement; statistical significance was set at p < 0.05. RESULTS: A total of 50 eyes were included in the study. There was a substantial agreement between stereoscopic 7F and montage (κ = 0.745, κweighted = 0.867) in assessment of DR severity. Of 50 eyes, 80% of the cases showed complete agreement, and 100% of the cases had agreement within one-step. There was a moderate agreement among graders, and κ-value ranged from 0.4705 to 0.5803. CONCLUSION: In this study, we found a substantial agreement in assessing DR severity score employing non-stereoscopic montage and stereoscopic 7F photographs.