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Hardness is one of the important attributes in determining the quality of dried fruits. Hardness assessment is normally carried out by manual inspection. This method is time consuming, laborious, expensive and subjective. The objective of this study was to develop a computer vision system with a monochrome camera to classify dates based on hardness. Date samples were obtained from three different growing regions in Oman and graded into soft, semi-hard, and hard classes based on hardness. A total of 1800 date samples were imaged individually using a monochrome camera (600 dates / class). Histogram and texture features were extracted from the acquired monochrome images and used in the classification models. The overall classification accuracies in three class model (soft, semi-hard, and hard) were 66% and 71% for linear discriminant analysis (LDA) and artificial neural network (ANN), respectively. It was improved to 84% and 77% in LDA and ANN, respectively while using two class model (soft and hard (semi-hard and hard together)). The histogram features were more contributing in the date classification based on hardness than image texture features. Computer vision technique has great potential to develop online quality monitoring systems for dates and other dried fruits.

Sustainable agricultural production could be assessed through energy-use efficiency (EUE). Thus, this paper aims to evaluate the EUE for cucumber greenhouse production in Oman. Data were obtained by interviewing farmers (face-to-face). Result indicated that total energy inputs (e.g. electricity, water, fertilizers and agrochemicals) and total energy output (cucumber yield) were 1159726.0 MJ ha-1 and 89942.9 MJ ha-1, respectively. The highest energy consuming input in the greenhouse production was electricity, consuming 88% of total energy input. This indicates that electricity had again the highest impact in cucumber greenhouse production and 99% of electricity goes for cooling the greenhouse. When all energy inputs were classified into its forms; direct (D) and indirect (ID), and renewable (R) and non-renewable (NR), the highest portion of total energy forms in greenhouse cucumber production was for D and NR energy. The EUE and energy productivity (EP) were found to be 0.07 and 0.10 kg MJ-1, respectively. Energy use in greenhouse cucumber production was inefficient and solar energy need to be implemented to improve cucumber greenhouse sustainability production.