World Health Organization (WHO) identifies anemia, a health hazard condition marked by the deficiency of red blood cells or hemoglobin in the blood stream, as maligning a quarter of the total world population. An automated, quick and reliable detection of anemia is hence imperative. Preliminary detection of anemia is usually undertaken visually by the physician by examining the color of the anterior conjunctiva of the eye and confirmed with an invasive blood test. In this study, we designed a mechanism for the automated detection of anemia through non-invasive visual method. Our process involves the detection of anemia by analyzing the anterior conjunctival pallor of the eye. It operates by quantifying the conjunctival color from digital photographs of the eye taken with a smartphone camera of appropriate resolution under adequate lighting conditions with the help of an android application that we have devised. These images are then processed to obtain the red and green component spectra of the conjunctiva color and compared against a threshold to determine whether the patient is anemic or not. We employed our method on 19 test subjects with known hemoglobin levels. The results obtained from our process agreed with the patient's blood report in 15 out of the 19 cases which translates to an accuracy of 78.9 percent. Our study was aimed towards the automation of healthcare facilities in underdeveloped parts of the world lacking proper healthcare facilities like hospitals and healthcare centers. Thus we developed a computerized, noninvasive, simple, cost effective, easy to use and portable primary screening test for anemia which can provide a viable alternative to invasive methods of anemia detection and have major humanitarian impact in the underdeveloped areas of the world.

New York state is among the largest producers of table beets in the United States, which, by extension, has placed a new focus on precision crop management. For example, an operational unmanned aerial system (UAS)-based yield forecasting tool could prove helpful for the efficient management and harvest scheduling of crops for factory feedstock. The objective of this study was to evaluate the feasibility of predicting the weight of table beet roots from spectral and textural features, obtained from hyperspectral images collected via UAS. We identified specific wavelengths with significant predictive ability, e.g., we down-select >200 wavelengths to those spectral indices sensitive to root yield (weight per unit length). Multivariate linear regression was used, and the accuracy and precision were evaluated at different growth stages throughout the season to evaluate temporal plasticity. Models at each growth stage exhibited similar results (albeit with different wavelength indices), with the LOOCV (leave-one-out cross-validation) R² ranging from 0.85 to 0.90 and RMSE of 10.81–12.93% for the best-performing models in each growth stage. Among visible and NIR spectral regions, the 760–920 nm-wavelength region contained the most wavelength indices highly correlated with table beet root yield. We recommend future studies to further test our proposed wavelength indices on data collected from different geographic locations and seasons to validate our results.

Cercospora leaf spot (CLS), caused by the fungus Cercospora beticola Sacc., is a severe foliar disease that affects the health of table beet crops. Remote sensing techniques, specifically unmanned aerial systems (UAS), could offer a valuable solution for evaluating disease progression and targeted fungicide application towards effectively managing CLS. We conducted UAS flights over C. beticola inoculated table beet plots at Cornell Agritech in Geneva, NY, USA during the growing seasons of 2021, 2022 and 2023. Five-band (475,560, 668, 717 and 840 nm) multispectral imagery was collected for the 2021 and 2022 flights, while for 2023 flights, a four-band (560, 650, 730 and 860 nm) sensor was used. CLS disease severity was assessed in situ at regular intervals, severity was evaluated using both spectral and textural features. Vegetation indices, derived from multispectral imagery, are known to be effective in assessing plant health and disease determination. Texture features also have been reported to be effective in determining plant traits since they provide additional pixel-spatial information to the model. Extensive selection was used to obtain the most relevant features. Our best-performing machine learning model used a random forest regressor to estimate disease severity at an R2test = 0.82 and RMSEtest = 9.31%. Furthermore, the disease progression was tracked using area-under-disease- progression-stairs (AUDPS) and the predicted values had a correlation coefficient of 0.84, 0.94 and 0.81 with 2021, 2022 and 2023 data, respectively. These results are strongly encouraging to support table beet growers with advanced data collection and analysis methodologies, enabling them to implement rapid and efficient foliar disease management strategies.