Solar energy is one of the most important solutions to reduce the concerns of the severe climate change phenomenon. Granted, the main manner to harness solar energy to generate power electricity is implemented through arrays made up of PV solar panels. However, the accumulation of dust on PV surfaces nevertheless remains a serious issue that considerably reduces the efficient conversion of PV panels. Therefore, this research is aimed at automating both monitoring and cleaning of the PV panel’s surfaces through the design, manufacture, and operation and evaluating a dry-cleaning robot based on a color monitoring system. The preliminary results demonstrate that the color analysis of the PV panels can distinguish between the density of dust accumulated, where the total color differences between the clean PV panels and both the PV panels with simple, moderate, and intense dust were 43.69, 61.19, and 75.23. This raised the efficiency of the power produced for simple dust panels from 88.03 to 98.91% (one cleaning round), moderate dust panels from 70.72 to 92.96%, and intense dust panels from 39.05 to 62.11% (two cleaning rounds). These preliminary finds illustrate the possibility of using this approach to automatic monitoring the PV panel color and operate the cleaning robot.

The assessment and assurance of the quality attributes of dates is a key factor in increasing the competitiveness and consumer acceptance of this fruit. The increasing demand for date fruits requires a rapid and automated method for monitoring and analyzing the quality attributes of date fruits to replace the conventional methods used by inspection which limits the production and involves human errors. Moisture content (MC), dry matter content (DMC), and firmness (F) are three important quality attributes for two date cultivars (Khalas and Sukkari) that have been inspected using the hyperspectral imaging (HSI) technique based on the reflectance mode. Images of intact date fruits at the maturity stage Tamr were obtained within the wavelength range of 950–1750 nm. Monitoring and assessment of MC, DMC, and F [first maximum rupture force (MF, N)] were performed using a partial least squares regression model. Accurate prediction models were attained. The results highlight that the coefficients of determination (R²_Prediction) are estimated to be 0.91 and 0.89 for MC, DMC, and F (N) with the lowest values of the standard error of prediction (SEP) equal to 0.82, 0.81 (%), and 4.12 (N), respectively, and the residual predictive deviation (RPD) values were 3.65, 3.69, and 3.42 for MC, DMC, and F (N), respectively. The results obtained from this preliminary study indicate the great potential of applying HSI for the assessment of physical, chemical, and sensory quality attributes of date fruits overall in the five maturity stages.

Harnessing solar energy is one of the most important practical insights highlighted to mitigate the severe climate change (CC) phenomenon. Therefore, this study aims to focus on the use of hybrid solar dryers (HSDs) within an environmentally friendly framework, which is one of the promising applications of solar thermal technology to replace traditional thermal technology that contributes to increasing the severity of the CC phenomenon. The HSD, based on a traditional electrical energy source (HS_TEE) and electrical energy from photovoltaic panels (HS_PVSE), was evaluated compared to a traditional electrical (TE) dryer for drying some medicinal and aromatic plants (MAPs). This is done by evaluating some of the drying outputs, energy consumed, carbon footprint, and financial return at 30, 40, and 50°C. The best quality of dried MAP samples in terms of essential oil (EO, %) and microbial load was achieved at 40°C. The HS_TEE dryer has reduced energy consumption compared to the TE dryer by a percentage ranging from 37% to 54%. The highest CO₂ mitigated ratio using the HS_TEE dryer was recorded in lavender, thyme, basil, lemongrass, and sage samples with values ranging from 45% to 54% at 30, and 50°C. The highest financial return obtained from energy consumption reduction and carbon credit footprint was achieved at 50°C, with values ranging from 5,313.69 to 6,763.03 EGP/year (EGP ≈ 0.0352 USD) when coal was used as a fuel source for the generation of electricity. Moreover, the HS_PVSE dryer achieved a 100% reduction in traditional energy consumption and then reduced CO₂ emissions by 100%, which led to a 100% financial return from both energy reduction and carbon credit. The highest financial returns were observed at 50°C, with values ranging from 13,872.56 to 15,007.02, 12,927.28 to 13,984.43, and 11,981.99 to 12,961.85 EGP/year (EGP ≈ 0.0352 USD) for coal, oil, and natural gas, respectively. The HS dryers show potential for environmental conservation contribution; furthermore, earning money from energy savings and carbon credit could help improve the living standards and maximize benefits for stakeholders.

Facing severe climate change, preserving the environment, and promoting sustainable development necessitate innovative global solutions such as waste recycling, extracting value-added by-products, and transitioning from traditional to renewable energy sources. Accordingly, this study aims to repurpose fish waste into valuable, nutritionally rich products and extract essential chemical compounds such as proteins and oils using a newly developed hybrid solar dryer (HSD). This proposed HSD aims to produce thermal energy for drying fish waste through the combined use of solar collectors and solar panels. The HSD, primarily composed of a solar collector, drying chamber, auxiliary heating system, solar panels, battery, pump, heating tank, control panel, and charging unit, has been designed for the effective drying of fish waste. We subjected the fish waste samples to controlled drying at three distinct temperatures: 45, 50, and 55 °C. The results indicated a reduction in moisture content from 75.2% to 24.8% within drying times of 10, 7, and 5 h, respectively, at these temperatures. Moreover, maximum drying rates of 1.10, 1.22, and 1.41 kgH₂O/kg dry material/h were recorded at 45, 50, and 55 °C, respectively. Remarkable energy efficiency was also observed in the HSD’s operation, with savings of 79.2%, 75.8%, and 62.2% at each respective temperature. Notably, with an increase in drying temperature, the microbial load, crude lipid, and moisture content decreased, while the crude protein and ash content increased. The outcomes of this study indicate that the practical, solar-powered HSD can recycle fish waste, enhance its value, and reduce the carbon footprint of processing operations. This sustainable approach, underpinned by renewable energy, offers significant environmental preservation and a reduction in fossil fuel reliance for industrial operations.

In light of the great technological progress in non-destructive quality detection methods, sweetness is no longer the essential parameter in evaluating watermelon quality. There is an aspiration to determine physicochemical quality characteristics to enable us to select the best cultivars, agricultural practices, and harvest dates. In the present work, three different watermelon cultivars (Lady, Galander, and Style) were harvested at three consecutive harvest times. Two pieces were taken from each watermelon sample, one from the middle (part A) and the other from the ends (part B), to track the intensity of quality parameters inside the watermelon. Parts A and B were subjected to Vis/NIR spectroradiometer (475:1075 nm), near-infrared spectroscopy (NIRS) (950:1650 nm), and high-performance liquid chromatography to assess the physicochemical quality. Calibration and prediction models were conducted using partial least squares regressions (PLS). The results indicated that the harvesting time significantly influenced the color and chemical parameters. Quality parameters concentrations markedly degraded towards late harvest. The highest concentrations of quality parameters were sighted for the middle zone (part A), especially in the Galander cultivar. Spectroradiometer achieved the best coefficient of prediction (R²_P) ≃ 0.88 and 0.81 attached with the lowest value of the standard error of prediction (SEP) ≃ 0.03 and 1.06 for chroma (C*) and yellowness index (YI). However, the findings showed the superiority of the NIRS compared to the Vis-NIR method. The highest R²_P was achieved by values 0.92, 0.91, 0.90, 0.89, 0.85, and 0.85 for lycopene, total carotenoids, vitamin C, β-carotene, γ-content, and TSS, respectively. It could be concluded that the NIRS has the ability to monitor the maturity development and determine the harvest dates practically and reliably.