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Abstract Manual observation bias in identifying cabbage leaf diseases necessitates efficient automated detection systems in agriculture. In this study, a deep learning-based approach was developed for the automatic recognition and categorization of cabbage diseases, focusing on aphids, worm cuts, and black rot. Using 1400 images, including healthy and diseased samples, our method employs novel deep learning models. EfficientNetB7, MobileNetV2, and DenseNet201 were utilized to successfully identify three types of cabbage leaf diseases with accuracies of 98.56%, 98.26%, and 97.64%, respectively. EfficientNetB7 achieved the highest accuracy of 98.56% for disease classification. The system's ability to automatically extract features from images enhances disease detection, crucial for mitigating yield losses. Through visualization techniques, we analyzed the location of unhealthy regions in the leaves, the filter, and the intermediate layer.

AbstractLinking farmers to high-value markets continue to be a viable option for breaking the food insecurity. Many studies have emphasized on factors determining smallholders’ participation in high-value markets. Previous studies have methodological limitations since they neglected the associated effects on food security. Therefore, the purpose this study was to fill a knowledge gap by investigating the factors influencing smallholder avocado farmers’ participation in high-value markets and how it impacts households’ food security in the Aleta chuko district, Ethiopia. The primary data was collected from randomly selected 389 avocado producers using a semi-structured questionnaire. Descriptive statistics, inferential statistics, and propensity score matching model were used to analyze the data. The result of the binary logit model revealed that the participation of avocado producers in a high-value market was influenced by age, educational status, the quantity of avocados sold, and price of avocado and market information. The ATT estimation of PSM model indicated that Avocado producers who participated in high-value market channels have higher food security status (by 4.3–4.8%) compared to those who were not. Thus, this study suggested that concerned bodies in Ethiopia should encourage more households to participate in the high-value markets.

Abstract Selecting appropriate market outlets offer the opportunity for farmers to capture a bigger share of the price paid by final consumers. However, smallholder farmers in developing countries are still confronted with myriad challenges regarding selecting profitable market outlets. Thus, the main objective of this study was to analyze determinants of market outlet choices by smallholder mango farmers in Aleta Chuko District, Sidama Region, Ethiopia. The study's representative 391 sample mango producers were selected using a multi-stage sample selection technique and a cross-sectional research approach. Descriptive statistics and the multivariate probit model were used to analyze the data. The result of the study showed that the probability of mango producers to choose consumer, collector, and retailer and wholesaler market outlets was 38%, 39.4%, 41% and 25%, respectively. This shows that retailer was the most likely chosen market outlet while wholesaler was the less likely chosen market outlet. The joint probability of farmers to choose the four market outlets is (0.042%) lower than the likely of not choosing four market outlets (23.21%). The result of the multivariate probit model showed that the sex of the household head, age of the household head, educational level of the household head, the quantity of mangoes produced, size of the land allocated for mango production, use of market information, credit access, livestock holdings, and distance to the nearest market were all statistically and significantly influencing factors in the market outlet choice behavior of mango producers. Based on the results of this study, the government and other concerned organizations should take action to improve the efficient marketing of mango producers in all outlets by providing market information, expanding and following up on extension services, increasing credit access, and building roads and other marketing infrastructure in the study area.

Abstract Health professionals need a strong prediction system to reach appropriate disease diagnosis, particularly for under-five child with health problems like anemia. Diagnosis and treatment delay can potentially lead to devastating disease complications resulting in childhood mortality. However, the application of machine learning techniques using a large data set provides scientifically sounded information to solve such palpable critical health and health-related problems. Therefore, this study aimed to determine the predictors of anemia among under-5 year’s age children in Ethiopia using a machine learning approach. A cross-sectional study design was done using the Ethiopian Demographic and Health Survey 2016 data set. A two-stage stratified cluster sampling technique was employed to select the samples. The data analysis was conducted using Statistical Package for Social Sciences/SPSS version 25 and R-software. Data were derived from Ethiopian Demographic and Health Survey. Boruta algorism was applied to select the features and determine the predictors of anemia among under-5 years-old children in Ethiopia. The machine learning algorism showed that number of children, distance to health facilities, health insurance coverage, youngest child’s stool disposal, residence, mothers’ wealth index, type of cooking fuel, number of family members, mothers’ educational status and receiving rotavirus vaccine were the top ten important predictors for anemia among under-five children. Machine-learning algorithm was applied to determine the predictors of anemia among under- 5 year’s age children in Ethiopia. We have identified the determinant factors by conducting a feature importance analysis with the Boruta algorithm. The most significant predictors were number of children, distance to health facility, health insurance coverage, youngest child’s stool disposal, residence, mothers’ wealth index, and type of cooking fuel. Machine learning model plays a paramount role for policy and intervention strategies related to anemia prevention and control among under-five children.

Abstract Background It is believed that in Ethiopia barley has been cultivated before 3000BC. Among the cereals, it is ranked in fifth place and the most important crop next to teff, sorghum, and wheat in the country. Different works of literature highlight that engagement in contract farming is one of the innovations promoted to tackle technology constraints of the smallholder farmers, as a possible solution to raising productivity and linking smallholders in the emerging modern marketing chains. Associated with the rapid rate of urbanization in the country, there is a high demand for malt barley which is resulting high expansion of beer factories. Consequently, in the study area, many farmers got into a contract agreement with Assela malt barley factories. Recent studies conducted on malt barley in Ethiopia gave much emphasis to the value chain aspect of malt barley. Therefore, the main motive behind this study was to fill the empirical literature gap in the field by giving much emphasis on the impact assessment of malt barley contract arrangement on income and food items dietary diversity of the respondent households. Methodology To attain the study objectives, both primary and secondary data were collected and used. Randomly selected 312 households comprising 127 households engaged in malt barley contract farming arrangements and 185 non-contract households were the source of primary data for this study. Secondary data were collected from a review of different works of literature. Both descriptive and econometric models were used to analyze the primary data using Stata software version 14. The propensity score matching model was applied to examine the impact of malt barley contract farming engagement on the income and dietary diversity of the respondents. Result It was found that family size, credit use, livestock holding, malt barley production experience, frequency of extension contact, and land allotted for malt barley production positively determine the probability of participation in malt barley contract farming arrangement. Contrary to this, distance to the malt barley collection centers negatively determine the probability of participation in malt barley contract farming. The ATT estimation of the PSM indicated that participation in malt barley contract farming has a positive impact on the income and dietary diversity of the respondent households. Conclusion Participation in contract farming had a positive and significant impact on the annual income and dietary diversity of the smallholder households. The sensitivity analysis result showed that the impact results estimated by this study are insensitive to unobserved selection bias and the result obtained shows the true impact of contract farming on the income of the households. Therefore, concerned bodies working on malt barley production aspects as a development intervention should work to encourage non-contract farmers to engage in this activity.

Studies on the level of knowledge, attitude and handling practice towards food safety would help to determine the associated factors of knowledge, attitude and practice towards food safety; however, these studies did not explicitly address food safety concerns related to fruit and vegetables in Ethiopia. Men and women could have different levels of knowledge, attitude, and handling practice; however, these gendered effects were not addressed in previous studies. Using data collected in 2021 from 311 farm households in Ethiopia, this study aimed to analyze the level of knowledge, attitude, and handling practice of fruit and vegetable and associated factors among male and female food handlers. Data was analyzed using descriptive statistics, spearman's correlation, and econometric models (logit, multinomial logit and ordered probit). Education has a positive significant effect on knowledge, attitude and handling practices of fruit and vegetable handlers. However, the effect was higher in the females. No correlation could be obtained between knowledge and practices for the total respondents. However, knowledge had a positive but weak significant association with practices in the female group. We found a significant positive correlation between knowledge and attitude. These findings indicated that food safety knowledge of fruit and vegetable handlers will influence their attitude and a positive attitude would influence the practice in safe handling of fruit and vegetables. It therefore may require more targeted campaigns (i.e. from awareness creation to behavior change) to increase the ability of the community members to adopt best practices while reducing the barriers associated with consuming unhealthy diets.