Design, construction and field testing of a manually feeding semiautomatic sugarcane dud chipper.

Sugarcane is the main sugar crop, and sugar is an important agricultural product in Egypt. There are many problems with the technology used in the current planting method of sugarcane, which has a great impact on the planting quality of sugarcane, which have a series of problems, such as low cutting efficiency and poor quality. Therefore, the aim of the current study was to design, construct, and field testing of a semiautomatic sugarcane bud chipper assisted with pivot knives for cutting sugarcane buds and germinating them in plastic trays inside a greenhouse until they reached an average length of 35 cm, and then planting them in the field. In the field tests five cutting speeds (35, 40, 45, 50, and 56 rpm. (Revolution Per minute), three cutting knives (1.5, 2.0, and 2.5 mm) were used for cutting sugarcane stalks with four different diameters (1.32, 1.82, 2.43, and 2.68 cm). The obtained results showed that the values of the damage index and invisible losses were within acceptable limits (ranging between - 1.0 and 0.0) for all the variables under the test. Still, the lowest damage index and invisible losses were recorded with the buds that were cut with a knife of 1.5 mm thickness and cutting speeds less than 50 rpm. The skipping rate increases with the increase in cutting speed and stalk diameter, ranging between 0.0 to 13%. The maximum machine productivity was 110 Buds per minute at a cutting speed of 35 rpm and stalk diameter of 1.32 cm. The paper's findings have important application values for promoting the designing and development of sugarcane bud chipper and sugarcane planting technology in the future.

Understanding the phenomenon of saltwater intrusion sourced from desalination plants at coastal aquifers.

Members of the Gulf Cooperation Council countries Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates rely on desalination to produce water for domestic use. Desalination produces brine that may intrude into the aquifers to pollute the fresh groundwater because of the concentration gradient and groundwater pumping. Modeling the trends of saltwater intrusion needs theoretical understanding and thorough logical experimentation. The objective of this exercise was to understand the phenomenon of saltwater intrusion using an existing set of data analyzed with the convective-diffusion equation and the two-region mobile-immobile solution model. The objective was achieved by optimizing non-measurable solute transport parameters from an existing set of data generated from a series of logical miscible displacements of potassium bromide through sepiolite minerals and curve-fitting simulations. Assumptions included that solute displacements through sepiolite porous media and the related simulations represented the phenomenon of saltwater intrusion under non-equilibrium conditions of porous media mimicking the aquifers. Miscible displacements of potassium bromide were observed from a column of 2.0-2.8 mm aggregates of sepiolite over 4 ranges of concentration and at 11 displacement speeds under saturated vertical flow deionized water and vice versa. Breakthrough curves of both bromide and potassium ions were analyzed by a curve-fitting technique to optimize transport parameters assuming solute movement was governed (i) by the convective-diffusion equation and (ii) the two-region mobile-immobile solution model. Column Peclet numbers from the two analyses were identical for potassium ions but those for bromide ions were c. 60% greater from the two-region model than from the convective-diffusion equation. For the two-region model, dispersion coefficients were well defined and remained unchanged from the convective-diffusion equation for potassium ions but decreased for bromide ions. Retardation factors for bromide ions were approximately the same, but those for potassium ions, though > 1, were poorly defined. In order to design mitigation strategies for avoiding groundwater contamination, this study's findings may help model groundwater pollution caused by the activities of desalination of seawater, which produces concentrated liquid that intrudes into the coastal aquifer through miscible displacement. However, robust saltwater intrusion models may be considered in future studies to confirm the results of the approach presented in this exercise. Field data on the groundwater contamination levels may be collected to compare with simulated trends drawn from the saltwater intrusion models and the curve-fitting technique used in this work. A comparison of the output from the two types of models may help determine the right option to understand the phenomena of saltwater intrusion into coastal aquifers of various characteristics.

Deep learning supported machine vision system to precisely automate the wild blueberry harvester header.

An operator of a wild blueberry harvester faces the fatigue of manually adjusting the height of the harvester's head, considering spatial variations in plant height, fruit zone, and field topography affecting fruit yield. For stress-free harvesting of wild blueberries, a deep learning-supported machine vision control system has been developed to detect the fruit height and precisely auto-adjust the header picking teeth rake position. The OpenCV AI Kit (OAK-D) was used with YOLOv4-tiny deep learning model with code developed in Python to solve the challenge of matching fruit heights with the harvester's head position. The system accuracy was statistically evaluated with R2 (coefficient of determination) and σ (standard deviation) measured on the difference in distances between the berries picking teeth and average fruit heights, which were 72, 43% and 2.1, 2.3 cm for the auto and manual head adjustment systems, respectively. This innovative system performed well in weed-free areas but requires further work to operate in weedy sections of the fields. Benefits of using this system include automated control of the harvester's head to match the header picking rake height to the level of the fruit height while reducing the operator's stress by creating safer working environments.

Response of varied rice genotypes on cell membrane stability, defense system, physio-morphological traits and yield under transplanting and aerobic cultivation.

Aerobic rice cultivation progresses water productivity, and it can save almost 50% of irrigation water compared to lowland rice with the appropriate development of genotypes and management practices. Two field trials were conducted during 2020, and 2021 seasons to determine the validation of different rice varieties under aerobic cultivation based on their plant defense system, physio-morphological traits, stress indices, grain yield, and water productivity. The experiments were designed in a split-plot design with four replications. Two planting methods, transplanting and aerobic cultivation, were denoted as the main plots, and ten rice genotypes were distributed in the subplots. The results revealed that the planting method varied significantly in all measured parameters. The transplanting method with well watering had the highest value of all measured parameters except leaf rolling, membrane stability index, antioxidant, proline, and the number of unfilled grains. EHR1, Giza179 and GZ9399 as well as A22 genotypes a chief more antioxidant defense system that operated under aerobic conditions. Giza179, EHR1, GZ9399, and Giza178 showed high cell membrane stability and subsequently high validation under such conditions, and also showed efficiency in decreasing water consumption and improving water use efficiency. In conclusion, this study proves that Giza179, EHR1, GZ9399, Giza178, and A22 are valid genotypes for aerobic conditions.

Impacts of COVID-19 pandemic on environment, society, and food security.

Coronavirus disease (COVID)-19 is a viral and transferable disease caused by severe respiratory syndrome-coronavirus-2. It can spread through breathing droplets in human beings. It caused 5.32 million deaths around the world at the end of 2021. COVID-19 has caused several positive impacts as well, such as a reduction in air, water, and noise pollution. However, its negative impacts are by far critical such as increased death rate, increased release of microcontaminants (pesticides, biocides, pharmaceuticals, surfactants, polycyclic aromatic hydrocarbons (PAHs), flame retardants, and heavy metals), increased biomedical waste generation due to excessive use of safety equipment and its disposal, and municipal solid waste generation. Environmental pollution was significantly reduced due to lockdown during the COVID-19 period. Therefore, the quality of air and water improved. COVID-19 affected all sections of the population, particularly the most vulnerable members of society, and thus pushed more people into poverty. At the world level, it increased risks to food safety by increasing prices and lowering revenues, forcing households to reduce their food consumption in terms of quantity and quality. COVID-19 also upset various exercises e.g., horticulture, fisheries, domesticated animals, and agribusiness hence prohibiting the development of merchandise for poor-country ranchers. Most of the patients can self-recover from COVID-19 if they do not have any other diseases like high blood pressure, diabetes, and heart problems. Predictably, the appropriate execution of the proposed approaches (vaccination, wearing face masks, social distancing, sustainable industrialization) is helpful for worldwide environmental sustainability.

Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks.

Streamflow (Qflow) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Qflow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Qflow time-series, while the LSTM networks use these features from CNN for Qflow time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Qflow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Qflow, the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Qflow prediction error below the range of 0.05 m3 s-1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Qflow prediction.

The assessment of emerging data-intelligence technologies for modeling Mg+2 and SO4-2 surface water quality.

The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.

Estimation of Soil Surface Roughness Using Stereo Vision Approach.

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

Thermal Discomfort Levels, Building Design Concepts, and Some Heat Mitigation Strategies in Low-Income Communities of a South Asian City.

Heat stress provokes thermal discomfort to people living in semiarid and arid climates. This study evaluates thermal discomfort levels, building design concepts, and some heat mitigation strategies in low-income neighborhoods of Faisalabad, Pakistan. The outdoor and indoor weather data are collected from April to August 2016 using a weather station installed ad hoc in urban settings, and the 52 houses of the five low-income participating communities living in congested and less environment-friendly areas of Faisalabad. The discomfort index values, related to the building design concepts, including (i) house orientation to sunlight and (ii) house ventilation, are calculated from outdoor and indoor dry-bulb and wet-bulb temperatures. Our results show that although June was the hottest month of summer 2016, based on the monthly mean temperature of the Faisalabad region, the month of May produced the highest discomfort levels, which were higher in houses exposed to sunlight and without ventilation. The study also identifies some popular heat mitigation strategies adopted by the five participating low-income communities during various heat-related health complaints. The strategies are gender-biased and have medical, cultural/customary backgrounds. For example, about 52% of the males and 28% of the females drank more water during dehydration, diarrhea, and eye infection. Over 11% and 19% of the males and females, respectively, moved to cooler places during fever. About 43% of the males and 51% of the females took water showers and rested to combat flu (runny nose), headache, and nosebleed. The people did not know how to cure muscular fatigue, skin allergy (from a type of Milia), and mild temperature. Planting trees in an area and developing open parks with greenery and thick canopy trees can be beneficial for neighborhoods resembling those evaluated in this study.

Ecological impacts of the N-viro biosolids land-application for wild blueberry (Vaccinium angustifolium. Ait) production in Nova Scotia.

Land application of biosolids from processed sewage sludge may deteriorate soil, water, and plants. We investigated the impact of the N-Viro biosolids land-application on the quality of the soil water that moved through Orthic Humo-Ferric Podzols soil of Nova Scotia (NS) at the Wild Blueberry Research Institute, Debert, NS Canada. In addition, the response of major soilproperties and crop yield was also studied. Wild blueberry (Vaccinium angustifolium. Ait) was grown under irrigated and rainfed conditions in 2008 and 2009. Four experimental treatments including (i) NI: N-Viro irrigated, (ii) NR: N-Viro rainfed, (iii) FI: inorganic fertilizer irrigated, and (iv) FR: inorganic fertilizer rainfed (control) were replicated 4 times under randomized complete block design. Soil samples were collected at the end of each year and analyzed for changes in cation exchange capacity (CEC), soil organic matter (SOM), and pH.Soil water samples were collected four times during the study period from the suction cup lysimeters installed within and below crop root zone at 20 and 40 cm depths, respectively. The samples were analyzed for a range of water quality parameters including conductance, hardness, pH, macro- and micronutrients, and the infectious pathogens Escherichia coli (E. coli) and salmonella. Berries were harvested for fruit yield estimates. Irrigation significantly increased CEC during 2008 and the soil pH decreased from 4.93 (2008) to 4.79 (2009). There were significant influences of irrigation, fertilizer, and their interaction, in some cases, on most of the soil water quality parameters except on the infectious bacteria. No presence of E. coli or salmonella were observed in soil and water samples, reflecting the absence of these bacteria in biosolids used in this experiment. Nutrient concentration in the soil water samples collected from the four treatments were higher in the sequence NI > NR > FI > FR. The irrigation treatment had significant effect on the unripe fruit yield. We conclude that the comparable performance of N-Viro biosolids and the increasing prices of inorganic fertilizers would compel farmers to use economically available N-Viro biosolids that, coupled with the supplemental irrigation, did not deteriorate the studied soil properties, soil water quality, and the wild blueberry yield during this experiment.