PV (photovoltaic) capacity is steadily increasing every year, and the rate of increase is also increasing. A desert area with a large equipment installation area and abundant solar radiation is a good candidate. PV power plants installed in the desert have advantages in themselves, but when combined with desert aquacultures, additional benefits can be obtained while compensating for the shortcomings of the aquaculture industry. The importance of the aquaculture industry is increasing, with aquaculture products approaching half of the total supply of marine products due to sea environmental pollution and reduced resources. Moreover, in deserts, where marine products are difficult to obtain, aquaculture is a good way to save marine products. However, one of the many problems that complicate the introduction of aquaculture in the desert is that it is difficult to supply and demand electricity because the site is not near a viable electric grid. However, combination with PV can solve this problem. This paper investigates the solar power and aquaculture industry in the desert and explains the limitations and challenges of the solar power and aquaculture industry in the desert. Based on this, we hope to increase interest in the solar power and aquaculture industry in the desert and help with future research.

The coffee industry contributes to the economic restructuring of many countries, often associated with a closed process from production to consumption. The green coffee bean grading standard provided by the Specialty Coffee Association (SCA) is one of the best methods for grading coffee beans. Traditionally, the assessment of quality and classification of coffee beans relies on visual examination, which demands significant time and effort and is easily inaccurate. Deep learning technology, characterized by precision, velocity, and veracity, can be adopted to empower the reduction of human labor and improve the productivity, quality, and efficiency of these tasks. Therefore, this paper aims to address these issues by implementing deep learning to classify coffee bean quality in real time by integrating the system with a cloud-based solution. First, image processing and data augmentation techniques are employed to handle the coffee bean image data. Subsequently, the model is trained using YOLOv8, a framework for object recognition, and OpenCV, an open-source image processing technology, to classify coffee beans. Finally, an application is developed for real-time video and image-streaming coffee bean recognition using React Native, NodeJS, and Python. The experimental results provide empirical evidence that our system enhances accuracy and efficiency in the tasks of classifying coffee bean quality in nine distinct varieties of coffee beans, with the time required reduced to a mere 1 to 3 seconds. Our system can be a useful solution for coffee producers, processors, and traders without relying on stationary equipment, especially in large farms or warehouses.

A concentration of airborne bacteria generated from swine houses is recognized to be relatively higher than other work places and it is essential to optimally manage it to prevent farmers' respiratory diseases. This study was conducted to assess the distribution characteristics of airborne bacteria in swine houses located at South Korea. A total 27 pig buildings of the enclosed type operated with mechanical ventilation system by a side wall fan and deep-pit manure system with slats were surveyed. Air samples were collected at 1.0 m above the middle floor in pig housing room. A six-stage viable particulate cascade impactor was used to identify the distribution of the sizes of particles in diameter. Seasonal mean levels of airborne bacteria in the housing rooms of gestation/farrowing pigs, nursery pigs and growing/fattening pigs were 3,428(±1,244) colony forming unit (cfu)/m3, 8,325(±3,209) cfu/m, and 13,254(±6,108) cfu/m3 for spring; 9,824(±2,157) cfu/m3, 18,254(±5,166) cfu/m3, and 24,088(±9,274) cfu/m3 for summer; 1,707(±957) cfu/m3, 4,258 (±1,438) cfu/m3, and 8,254(±2,416) cfu/m3 for autumn; and 2,322(±1,352) cfu/m3, 6,124(±1,527) cfu/m3 and 12,470(±4,869) cfu/m3 for winter, respectively. Concentrations of airborne bacteria according to pig housing type were highest in growing/fattening housing room followed by nursery housing room and gestation/farrowing housing room. In terms of seasonal aspect, the pig building showed the highest levels of airborne bacteria in summer followed by spring, winter and autumn. The respirable airborne bacteria which are ranged between 0.6 and 4.7 μm accounted for approximately 60% compared to total airborne bacteria regardless of pig housing type.

Recently, the interest in the plant factory-based crop production technologies is rising following the application of the smart farm technology to the agricultural arena. A lettuce production system platform is proposed in this study considering the effects of indoor environmental conditions and artificial light sources. The spectral characteristics of a visible ray according to growth performances were analyzed first to develop a control algorithm that can stimulate the plant’s growth for the proposal. Secondly, an imaging system was designed to analyze the plant’s growth characteristics based on the images and set up the system configuration. Lastly, a crop production system was proposed by using an experimental crop production group for environmental control and monitoring.

The dust generated from poultry houses has an adverse effect on farmers and poultry in terms of hygiene and welfare problems. However, there is little information on concentration and emission of dust derived from poultry houses located in South Korea. An objective of this study is to provide fundamental data regarding particulate matters generated from the poultry houses situated in South Korea. A total 27 poultry houses, including nine broiler houses, nine layer houses, and nine layer houses with feces conveyors were surveyed. Dust was measured by gravimetric methods. Emission of dust was calculated by multiplying the mean concentration (mg/m³) measured at the center of the poultry house by the ventilation rate (m³ h⁻¹). Mean indoor concentrations of total and respirable dust in poultry houses were 4.39 (SD: 2.38) mg/m³ and 2.33 (SD: 2.21) mg/m³, respectively. Mean emission rates based on area and rearing number were estimated as 3.04 (±1.64) mg head⁻¹ h⁻¹ and 57.48 (±24.66) mg m⁻² h⁻¹ for total dust and 2.34 (±1.27) mg head⁻¹ h⁻¹ and 26.80 (±10.81) mg m⁻² h⁻¹ for respirable dust, respectively. The distribution of total and respirable dust between indoor concentration and emission rate was a similar pattern, regardless of type of poultry house. Among types of poultry house, the broiler house showed the highest levels of indoor concentration and emission rate, followed by the layer house with feces conveyor belt, and the caged layer house. In terms of seasonal aspect, indoor concentrations of total and respirable dust were highest in winter and lowest in summer, and their emission rates were the opposite at all the poultry houses. In spring and autumn, both indoor concentration and emission rate were moderate, and there was no significant difference between spring and autumn. It was assumed that the levels of indoor concentration and emission rate of dust generated from poultry houses were determined mainly by use of bedding material and ventilation rate among various environmental agents.