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The objective of this study was to develop an artificial intelligence (AI)-driven model for the detection of helmet usage among workers in tractor and agricultural machinery factories with the aim of enhancing occupational safety. A transfer learning approach was employed, utilizing nine pre-trained neural networks for the extraction of deep features. The following neural networks were employed: MobileNetV2, ResNet50, DarkNet53, AlexNet, ShuffleNet, DenseNet201, InceptionV3, Inception-ResNetV2, and GoogLeNet. Subsequently, the extracted features were subjected to iterative neighborhood component analysis (INCA) for feature selection, after which they were classified using the k-nearest neighbor (kNN) algorithm. The classification outputs of all networks were combined through iterative majority voting (IMV) to achieve optimal results. To evaluate the model, an image dataset comprising 662 images of individuals wearing helmets and 722 images of individuals without helmets sourced from the internet was constructed. The proposed model achieved an accuracy of 90.39%, with DenseNet201 producing the most accurate results. This AI-driven helmet detection model demonstrates significant potential in improving occupational safety by assisting safety officers, especially in confined environments, reducing human error, and enhancing efficiency. Full article

The main global challenge nowadays is how to achieve food security with sharp population growth by considering long-term sustainability. Adding value to many agricultural products can improve product quality and farmer income, minimize waste, and address food security issues towards sustainability. In Oman, date palm is the most cultivated and consumed crop and has a high percentage of postharvest losses, which provokes more focus on arranging strategies to improve date production with quality and high productivity. This study aimed to develop guidelines and procedures for the value addition of dates in Oman, taking into account different farm categories (individual, group, SME) and four mechanization levels based on machinery used in different processing steps. Six date factories engaged in value addition in Oman and three popular value-added products from different date varieties were selected for the study. Nine value-addition guidelines/procedure sheets were developed, each with 13 features such as the mechanization level of each process, investment, technology transfer, and capacity-building needs. Among the results, the guidelines/procedure sheets for dates with nuts under the individual farm category of areas up to 0.84 ha and mechanization levels 1 and 2 will need an initial capital investment of 1500–3000 OMR, and the average value-addition benefit could reach a productivity uplift of up to 165% with 4550–7850 OMR annual net profit. The nine developed guidelines/procedure sheets will provide decision-making support for farmers, producers, and extension officers, and will contribute to improving product quality, farm income, productivity, and agricultural sustainability. The developed sheets will provide country-specific protocol developments and a significant contribution from this study is that all stakeholders are expected to benefit. Full article