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This work aims to inspect tomato features and classify them based on color and morphological features into the three predefined regions using artificial neural networks (ANN). Different learning methods were analyzed for the task of inspecting tomatoes using image processing software in MATLAB. Tomatoes were collected from the eastern parts of Ethiopia. The neural classification was done by the shape and size feature alone. The ANN classifier on the selected color feature alone showed that from the total test examples of 180 images, 168 (93.3 %) were correctly classified and 12 (6.7 %) were misclassified. The ANN classifier on all features taken together showed that all the test images were correctly classified. This result is similar to the morphology (shape and size) features result, but if the number of data points is high, the result may vary significantly. The overall result revealed that shape and size features have more discriminating power than color features, and the discrimination power increases when individual features are trained together with shape and size features. This may be because the discriminating factor increases due to the increase in the number of included features. It was observed that the proposed method was successful as quantified by the cumulative error (CE) and percentage error (%E) of training, testing, and validation of color features: 6.35 %, 3.70 %, and 11.11 %, respectively, in evaluating the quality of tomatoes. [ABSTRACT FROM AUTHOR]

Wheat rusts are the key biological constraint to wheat production in Ethiopia—one of Africa's largest wheat producing countries. The fungal diseases cause economic losses and threaten livelihoods of smallholder farmers. While it is known that wheat rust epidemics have occurred in Ethiopia, to date no systematic long-term analysis of past outbreaks has been available. We present results from one of the most comprehensive surveillance campaigns of wheat rusts in Africa. More than 13,000 fields have been surveyed during the last 13 years. Using a combination of spatial data-analysis and visualization, statistical tools, and empirical modelling, we identify trends in the distribution of wheat stem rust (Sr), stripe rust (Yr) and leaf rust (Lr). Results show very high infection levels (mean incidence for Yr: 44%; Sr: 34%; Lr: 18%). These recurrent rust outbreaks lead to substantial economic losses, which we estimate to be of the order of 10s of millions of US-D annually. On the widely adopted wheat variety, Digalu, there is a marked increase in disease prevalence following the incursion of new rust races into Ethiopia, which indicates a pronounced boom-and-bust cycle of major gene resistance. Using spatial analyses, we identify hotspots of disease risk for all three rusts, show a linear correlation between altitude and disease prevalence, and find a pronounced north-south trend in stem rust prevalence. Temporal analyses show a sigmoidal increase in disease levels during the wheat season and strong inter-annual variations. While a simple logistic curve performs satisfactorily in predicting stem rust in some years, it cannot account for the complex outbreak patterns in other years and fails to predict the occurrence of stripe and leaf rust. The empirical insights into wheat rust epidemiology in Ethiopia presented here provide a basis for improving future surveillance and to inform the development of mechanistic models to predict disease spread. [ABSTRACT FROM AUTHOR]

Objective: To determine the yield and determinants of retrospective TB contact investigation in selected zones in Ethiopia. Materials and Methods: This was a community-based cross-sectional study conducted during June-October 2014.Trained lay providers performed symptom screening for close contacts of index cases with all types of TB registered for anti-TB treatment within the last three years. We used logistic regression to determine factors associated with TB diagnosis among the contacts. Results: Of 272,441 close contacts of 47, 021 index cases screened, 13,886 and 2, 091 had presumptive and active TB respectively. The yield of active TB was thus 768/100, 000, contributing 25.4% of the 7,954 TB cases reported from the study zones over the study period. The yield was highest among workplace contacts (12,650/100, 000). Active TB was twice more likely among contacts whose index cases had been registered for TB treatment within the last 12 months compared with those who had been registered 24 or more months earlier (adjusted odds ratio, AOR: 1.77 95% CI 1.42–2.21). Sex or clinical type of TB in index cases was not associated with the yield. Smear negative (SS-) index cases (AOR: 1.74 955 CI 1.13–2.68), having index cases who registered for treatment within <12 months (AOR: 2.41 95% CI 1.51–3.84) and being household contact (AOR: 0.072 95% CI 0.01–0.52) were associated with the occurrence of active TB in children. Conclusions: The yield of retrospective contact investigation was about six times the case notification in the study zones, contributing a fourth of all TB cases notified over the same period. The yield was highest among workplace contacts and in those with recent past history of contact. Retrospective contact screening can serve as additional strategy to identify high risk groups not addressed through currently recommended screening approaches. [ABSTRACT FROM AUTHOR]