Fruits are extremely fundamental in our everyday diet. The palatable fruits are harvested, sorted, and packed for conveyance to the consumers. It needs a large number of expert resources and a long time to sort and grade the fruits from the agricultural field to the fruit markets. Automation in the agricultural field and fruit markets is a must to reduce the time as well as the dependency on the manual resource. Thus, the objective of this project is to fully automate the sorting process of handling fruits. The proposed method used an image acquisition system (camera), which acquires the images of the various selected fruits (Apple, Onion, Banana, Pepper and Tomato) used for the training data. The textural and colour features of the selected fruits were extracted and then, processed using the MATLAB software with Support vector machine (SVM) algorithm as the classifier. The fruit recognition system classified the input fruit sample by determining the similarities between the colour and gray level co-occurrence matrix values of the inputted fruits samples and the values obtained from the training datasets. The proposed method is accurate and flexible. Also, a graphical user interface was developed to be used independently of the software, the recognition rate of the system had an average accuracy of 97%.
This study examined effectiveness of mobile phone utilisation for agricultural information dissemination among tomato farmers in Ondo State, Nigeria, described their socioeconomic characteristics, assessed their knowledge, and identified constraints to mobile phones utilisation. Quantitative data was obtained from 100 tomato farmers through a three-stage sampling procedure. Chi-square analysis was used to test the relationship between respondents’ socioeconomic characteristics and effectiveness of utilisation of mobile phones. The results revealed that 94% of respondents used mobile phones, 66% utilised their mobile phones for agricultural purposes, 62% had internet-enabled services on their phones, and 51% indicated that mobile phones impacted their tomato production activities. The severe constraints faced by the respondents were "no light to charge phone" (x? = 2.75; ? = 0.87) and "network issues” (x? = 2.68; ? = 0.76). There was a significant relationship between age (?2=38.720; p< 0.05), marital status(?2=270.440;p<0.05), family size (?2=63.061;p< 0.05), secondary occupation (?2=58.080;p<0.05), level of education (?2=71.703;p< 0.05), sources of labour (?2=45.200;p< 0.05), sources of credit (?2=174.960;p< 0.05), monthly income (?2=63.900;p< 0.05) and effectiveness of mobile phone technology (?2=58.080;p< 0.05). The study recommended upgrading network infrastructure, enhancing affordability of mobile services, developing user-friendly customized applications and fostering digital literacy among the farmers
Grain trade plays a key role in global food security, but it faces a number of challenges, including climate change, geopolitical conflicts, and market volatility. These challenges have led to increasing attention on the agricultural applications of artificial intelligence (AI). AI technologies (such as predictive analytics, automated logistics, and precision agriculture) are helping to increase efficiency, reduce losses, and enhance sustainability. The aim of this article is to examine the role of AI in grain trade and its future potential.
Food waste has become a pressing global sustainability challenge, reflecting both environmental degradation and inefficient resource utilization. This study explores the relationships between attitudes, subjective norms, and perceived behavioral control (PBC) toward food waste reduction in the Kurdistan Region of Iraq, guided by the Theory of Planned Behavior (TPB). A quantitative approach was applied using an online survey of 205 respondents from Duhok City, employing a convenience sampling technique. Data were analyzed using SPSS version 26 through descriptive statistics, reliability and validity tests (Cronbach’s alpha, KMO, and Bartlett’s test), and multiple regression analysis. The results revealed that attitudes (? = 0.46, p < .001) and subjective norms (? = 0.30, p < .001) significantly influence perceived behavioral control (R² = 0.314), indicating that moral attitudes and social influence are key predictors of individuals’ perceived capacity to reduce food waste. The study confirms the applicability of the Theory of Planned Behavior in understanding food waste reduction–related perceptions in emerging economies and highlights the importance of public education, social campaigns, and household awareness programs. The findings provide valuable insights for policymakers, educators, and environmental organizations seeking to foster behavioral change and sustainability in the Kurdistan Region of Iraq.
The management of Waste Electrical and Electronic Equipment (WEEE) remains a critical sustainability challenge across the European Union (EU). Although the WEEE Directive 2012/19/EU predates the United Nations Sustainable Development Goals (SDGs), its progressively stricter collection targets, from 45% to 65%, are closely aligned with global objectives on sustainable production and consumption. This study develops an integrated predictive framework to forecast the EU27 WEEE Collection Rate (CR) from 2022 to 2030. The framework draws on four modeling families: Statistical methods, Machine Learning (ML) algorithms, Deep Learning (DL) architectures, and selected Hybrid configurations. Data for “Waste Collected” (COL) and “Products Put on the Market” (MKT) were obtained from Eurostat, with missing values imputed through linear interpolation validated against external socioeconomic indicators. Among all models tested, Lasso and Ridge Regression achieved the most accurate forecasts for the COL and MKT datasets, respectively. Although a Hybrid model was implemented to address non-linear residual patterns, it did not outperform the standalone Lasso model, which was retained for CR estimation. The resulting forecasts reveal a consistent downward trend in the Collection Rate, remaining below the 65% target throughout the forecast horizon. This shortfall is primarily driven by an accelerating volume of EEE placed on the market that is not matched by proportional increases in WEEE collection. The findings highlight systemic gaps in current collection mechanisms and underscore the need for enhanced policy interventions. The proposed framework offers a replicable and empirically validated tool to support evidence-based planning and regulatory monitoring in alignment with EU environmental policy and the Sustainable Development Goals.
