Search Results (306)

This review aims to provide a comprehensive overview of nano- and microscopic materials that can provide thermal radiation insulation without reducing visible light transmittance, thereby reducing heat loss and conserving energy in greenhouses. We also reviewed the radial and thermal properties of greenhouse covering materials. Fillers, colorants, reinforcers, and additives, as well as glass, plastic film, and plastic sheet materials, were discussed. Additionally, by searching for keywords like insulation film, insulation agent, and infrared insulation, compounds based on graphene and fullerene as well as phase transition materials (PCMs) that may be used for radiation insulation, we proposed their potential use in greenhouse covers. They can be divided into semi-transparent photovoltaic (PV) materials, zinc oxide-based film fillers, and silica filter films. We discussed the radiation heat insulation and light transmission characteristics of these materials. Nano-synthesis techniques were also investigated. Based on latest advances in the literature, future developments in the micro- and macroscale synthesis of nanomaterials will enable additional innovations in covering materials for greenhouse structures. A limiting factor, though, was the high sensitivity of PVs to external climatic and meteorological variables. The ability of materials used to make greenhouse covers to control the microclimate, reduce CO₂ emissions, use less energy, and increase agricultural productivity, however, cannot be disputed. Similar to this, a thorough examination of the uses of various greenhouse technologies reveals that the advancements also have financial advantages, particularly in terms of reducing greenhouse heating and cooling expenses. The PCMs, which decreased greenhouse-operating costs by maintaining constant ambient temperatures, provide ample evidence of this. Full article

Like most crops, sugarcane needs to be kept upright until it is harvested. The lodging of sugarcane has significant negative effects on the cane yield and sugar content of sugarcane. To keep sugarcane upright, earthing up is an essential in the cultural part of the operation. In Bangladesh, most of the sugarcane cultivation operations, including earthing-up, are generally performed in a traditional manual method which increases the production costs as well as reduces the income of sugarcane growers. Therefore, a cost-effective two-wheeled tractor (2WT)-mounted earthing-up machine was developed at the Bangladesh Sugarcrop Research Institute (BSRI), Pabna, to reduce drudgery and the cost of sugarcane production. Field tests were conducted in an experimental sugarcane field at BSRI and technical and economic performances of the developed earthing-up machine were also carried out based on the field test. The average effective field capacity and field efficiency of the earthing-up machine were found to be 0.16 ha/h and 77.41%, respectively. The 2WT-driven earthing-up machine was not found to be economically viable when it was used only for earthing-up operations. However, when the 2WT was used as the main driver for other activities, including earthing-up operation, the earthing-up machine became economically beneficial with net cash flow (NCF), net present value (NPV), internal rate of return (IRR), benefit–cost ratio (BCR), and payback period (PP) of BDT 148,497/ha, BDT 23,184, 3%, 3.81:1, and approximately 1 year, respectively. On the contrary, considering the cost of only earthing-up tool without 2WT, it was found to be economically beneficial with NCF, NPV, IRR, BCR, and PP of BDT 16,428/ha, BDT 3053, 4.7%, 2.71:1, and approximately 2 years, respectively. In Bangladesh, 2WT is commonly used for versatile farming purposes. Therefore, the versatile use of 2WT as a prime mover for other machines, including the earthing-up machine, can make earthing-up machine economically viable and beneficial for sugarcane growers in Bangladesh. Full article

The purpose of milking machines is to harvest milk at optimal quality and speed, while maintaining animal comfort and teat defense mechanisms against invading mastitis pathogens. Therefore, the milking machine is a very important piece of equipment on dairy farms to maintain a long healthy lactation by following the physiological conditions of the udder. The mechanical forces during long-term machine milking processes lead to changes in the teat tissue. This effect is related to the degree of adaptation of the milking machines to the physiological requirements of the individual udder anatomy and the physiological conditions of the lactating animals. If both, milking machine settings and liner design are not suitable for all teats and animals on the farm, some animals will not be fully milked, the teat condition will deteriorate over time and in the end, they may suffer from mastitis. Therefore, maintaining healthy udders and teats during milking is a central key component of an effective milking machine to produce good milk yield with higher quality by preventing mastitis and maintaining animal health and welfare. On large and thick teats, conventional liners often fit too tight, causing a massive mechanical stress load on the tissue. On small teats, however, they often do not adhere sufficiently close to the teat which can cause a considerable air admission and hence liner slips. The new liners, “Stimulor^® StressLess” (Siliconform, Türkheim, Germany), have a wave-like lip construction and adapt well to the different teat sizes in a herd, thus ensuring consistent milking of lactating animals. A proper milking machine accommodates all teat sizes and forms, has a low vacuum to effectively open the teat and to stimulate physiological milk release and letdown. In addition, the right pulsation rate will maintain a stable vacuum on the teat area during milking. In conclusion, an ideal milking machine adapts to the morphological, anatomical, and physiological characteristics of the udder and teats of the lactating animals and it should achieve a physiologically ideal milking process that meets high animal welfare standards and increases milk production with a high quality standard. Full article

The application of precision agriculture in cocoa and papaya cultivation in Brazil is still incipient. This study aimed to evaluate the spatial variability of the physical attributes of soil cultivated with a consortium of papaya and cocoa. The study was conducted in two sampling grids of 50 points, in two areas cultivated with papaya and cocoa with different planting times (three and eleven months). The soil attributes soil resistance to penetration (RP) and soil gravimetric moisture (UG) were determined at soil depths of 0–20, 20–40 and 40–60 cm. The data were submitted to an exploratory and descriptive analysis. Subsequently, a geostatistical analysis was performed to quantify spatial dependence and then interpolation of the data through kriging. The maps showed weak spatial variability for the UG and RP. In the two areas, it was observed that the depth of 0–20 cm had a lower RP (1.7 Mpa) and a higher UG (40 g g⁻¹), and as the depth was higher, had a higher RP (4.4 Mpa) and a lower UG (38 g g⁻¹). Area 1 presented higher RP values in depth, showing greater susceptibility to compaction. The area characterized by the consortium of papaya and cocoa presented more susceptible to compaction. The mechanical resistance of the soil to penetration was more critical in the 40–60 cm layer for the two consortia evaluated, evidencing areas with possible restriction to plant growth. Full article

In a world in continuous evolution and in which human needs grow exponentially according to the increasing world population, the advent of new technologies plays a fundamental role in all fields of industry, especially in agriculture. Optimizing times, automating machines, and guaranteeing product quality are key objectives in the field of Agriculture 4.0, which integrates various innovative technologies to meet the needs of producers and consumers while guaranteeing respect for the environment and the planet’s resources. In this context, our research aims to propose an integrated system using data coming from an innovative experimental atmospheric and forecasting simulator (capable of predicting some characteristic climate variables subsequently validated with local sensors), combined with indices deriving from Remote Sensing and UAV images (treated with the data fusion method), that can give fundamental information related to Agriculture 4.0 with particular reference to the subsequent phases of system automation. These data, in fact, can be collected in an open-source GIS capable of displaying areas that need irrigation and fertilization and, moreover, establishing the path of an automated drone for the monitoring of the crops and the route of a self-driving tractor for the irrigation of the areas of interest. Full article

The global cultivation of blood oranges is experiencing an increase due to their remarkable nutritional properties. Blood orange by-products, especially the peel, have a high concentration of bioactive compounds with exceptional antioxidant potential, making them an ideal choice for incorporation into various food products. This study aimed to determine the morphological parameters and primary and secondary metabolite content of peel of eight blood orange varieties using ¹H NMR and HPLC-ESI-DAD-MSⁿ. “Tarocco Meli” had the highest weight (367.83 g), caliber (94.13 mm and 88.87 mm), peel thickness (6.73 mm), and peel weight (155.0 g). “Tarocco Rosso”, “Sanguinelli”, and “Tarocco Gallo” had the highest levels of total amino acids (25.57 g kg⁻¹ DW), total organic acids (29.99 g kg⁻¹ DW), and total sugars (68.56 g 100 g⁻¹ DW), respectively. The peel of “Moro” had significantly higher concentrations of total anthocyanins, hydroxycinnamic acids, and flavones (650.67, 263.33, and 449.85 mg kg⁻¹, respectively) compared to the other varieties. In conclusion, “Tarocco Meli” had the most interesting values for morphological parameters, “Tarocco Rosso”, “Sanguinelli”, and “Tarocco Gallo” for primary metabolites, and “Moro” for secondary metabolites. With the increasing interest in utilizing co-products, these findings could be useful in developing functional food products that meet consumer demands for healthier and more sustainable food choices. Full article

Plastic contamination in marketable cotton bales, predominantly from plastic wraps used in John Deere round module harvesters, poses a significant challenge to the U.S. cotton industry. Despite rigorous manual efforts, the intrusion of plastic into the cotton gin’s processing system persists. We have developed a machine-vision detection and removal system aimed at mitigating this problem. This system employs inexpensive color cameras to detect plastic on the gin-stand feeder apron and subsequently removes it, reducing contamination. This system, built around low-cost ARM computers running Linux, comprises 30–50 machines and requires considerable effort to calibrate and tune. Moreover, its operation represents a technological challenge to typical cotton gin workers. This research presents a solution to this calibration operational hurdle by introducing an auto-calibration algorithm that has potential to simplify the system into a plug-and-play device. The auto-calibration system is designed to dynamically track the cotton color and utilizes frequency statistics to avoid plastic images that could compromise the system’s performance if incorporated into the auto-calibration process. We detail the design of the auto-calibration algorithm, which is expected to significantly reduce the setup overhead and facilitate the system’s continuous use. This innovation minimizes the need for skilled personnel and, therefore, is expected to expedite the system’s adoption across the cotton ginning industry. Full article

The purpose of this article is to identify opportunities that digital transformation in post-harvest activities offers to an engineering solution provider. The research method is a simple case study. The object is a company based in southern Brazil that provides engineering-integrated digital solutions to grain producers, including products and services. The specific objectives are to describe the company’s digital products and services, identify opportunities and players, and discuss how players can take advantage of opportunities owing to business process digitalization. The main results include separating products into three technological layers and identifying five types of opportunities (financing, commercialization, operation, logistics, traceability, and insurance), eight types of players, and the main opportunities for each player. The most significant opportunities are risk reduction in insurance contracts, improvement in grain quality, increments in food safety, and accurate information on grain movements. The main implication of the study is that grain producers and other players can explore opportunities, and solution providers can evolve toward complete digitalization by integrating service into the current offerings of post-harvest engineering solutions. Full article

Fusarium ear rot (FER) is a common disease in maize caused by the pathogen Fusarium verticillioides. Because of the quantitative nature of the disease, scoring disease severity is difficult and nuanced, relying on various ways to quantify the damage caused by the pathogen. Towards the goal of designing a system with greater objectivity, reproducibility, and accuracy than subjective scores or estimations of the infected area, a system of semi-automated image acquisition and subsequent image analysis was designed. The tool created for image acquisition, “The Ear Unwrapper”, successfully obtained images of the full exterior of maize ears. A set of images produced from The Ear Unwrapper was then used as an example of how machine learning could be used to estimate disease severity from unannotated images. A high correlation (0.74) was found between the methods estimating the area of disease, but low correlations (0.47 and 0.28) were found between the number of infected kernels and the area of disease, indicating how different methods can result in contrasting severity scores. This study provides an example of how a simplified image acquisition tool can be built and incorporated into a machine learning pipeline to measure phenotypes of interest. We also present how the use of machine learning in image analysis can be adapted from open-source software to estimate complex phenotypes such as Fusarium ear rot. Full article

Physical classification is the procedure adopted by the rice unloading, delivery, storage, and processing units for the commercial characterization of the quality of the grains. This step occurs mostly by the conventional method, which demands more time and specialized labor, and the results are subjective since the evaluation is visual. In order to make the operation faster, more accurate, and less dependent, non-destructive technologies and computational intelligence can be applied to characterize grain quality. Therefore, this study aimed to characterize and predict the quality of whole, processed rice grains, as well as classify any defects present. This was achieved by sampling from the upper and lower points of four silo dryers with capacities of up to 40,000 sacks. The grain samples had moisture contents of 16%, 17%, 18%, and 19% and were subjected to drying-aeration until reaching 12% moisture content (w.b.). Near-infrared spectroscopy technology and Machine Learning algorithm models (Artificial Neural Networks, decision tree algorithms Quinlan’s algorithm, Random Tree, REPTree, and Random Forest) were employed for this purpose. By analyzing Pearson’s correlation statistics, a strong negative correlation (R² = 0.98) was found between moisture content and the yield of whole grains. Conversely, a strong positive correlation (R² = 0.97) was observed between moisture content and classified physical defects across the various characterized physicochemical constituents. These findings indicate the effectiveness of near-infrared spectroscopy technology. The Random Tree model (RandT) successfully predicted the grain quality outcomes and is therefore recommended as the model of choice, obtained Pearson’s correlation coefficient (r = 0.96), mean absolute error (MAE = 0.017), and coefficient of determination (R² = 0.92). The results obtained here reveal that the combination of near-infrared spectroscopy technology and Machine Learning algorithm models is an excellent non-destructive alternative to manual physical classification for characterizing the physicochemical quality of whole and defective rice grains. Full article

Nutrient availability plays a crucial role in microalgae growth in domestic wastewater. In this study, we investigated the impact of different nitrogen and phosphorus ratios (5:1, 10:1, and 20:1, m∙m⁻¹), and the addition of inorganic carbon on microalgae growth and nutrient uptake from domestic wastewater. Microalgae biomass achieved values ranging from 0.54 to 1.41 g·L⁻¹. The cultivation process had maximum removal efficiencies of 83.7% for soluble chemical oxygen demand (sCOD), 74.0% for total Kjeldahl nitrogen (TKN), and 100.0% for ammonia (NH₃) and orthophosphate (PO₄³⁻). All the NH₃ and PO₄³⁻ concentrations from domestic wastewater without supplementation were completely removed on the fourth day of cultivation. Moreover, no significant differences in microalgae growth, and NH₃ and PO₄³⁻ removals were observed between the conditions with and without nutrient supplementation on the fourth day of cultivation. This study has shown the feasibility of growing microalgae in domestic wastewater without any nutritional supplementation. Further investigations are required to check the long-term performance, energy requirements, and economic viability of this system for wastewater treatment and the production of nutrient-rich biomass for agricultural applications. Full article

Building machine learning (ML) calibrations using near-infrared (NIR) soil spectroscopy direct in agricultural areas (online NIR), soil attributes can be fine-scale mapped in a faster and more cost-effective manner, guiding management decisions to ensure the maintenance of soil functions. However, a financially and environmentally unattractive density of 3–5 laboratory soil samples per ha is required to build these calibrations. Since no reports have evaluated if they are reusable or if a new calibration is required for each acquisition, this study’s objective was to acquire online NIR spectra in an agricultural field where ML models were previously built and validated, assessing their performance over time. Two spectral acquisitions were held over a fallow tropical field, separated by 21 days. Soil properties (clay, organic matter, cation exchange capacity, pH, phosphorus, potassium, calcium, and magnesium) were predicted using principal components regression models calibrated with day 1 spectra. Day 1 and day 21 predicted values and maps interpolated by ordinary kriging were compared. Spectra characteristics (morphology, features, and intensity) were evaluated. Predicted values from the two days were not correlated, as no causal relationship was found for the only Pearson’s correlation coefficient (r) significative at 99% (p < 0.01) (calcium, with r = 0.22 in the comparison pairing the nearest neighbors from the two days). For clay, organic matter, and cation exchange capacity, despite their robust prediction on day 1, no significative r values were found, ranging from −0.14 to 0.32, when comparing day 1 with day 21. The maps of the two days presented no similar spatial distribution, hindering their use for management decisions. Soil moisture is a suggested source of variation, but the analysis indicated that it was not the only one, requiring further investigation of the effect of soil surface conditions and environmental variables. Although further investigations should be performed, the results presented suggest that online NIR spectra ML models require spatio-temporal local calibrations to perform properly. Full article

This article presents a study on the spatiotemporal dynamics of land cover and use, vegetation indices, and water content in the semiarid region of Pernambuco, Brazil. This study is based on an analysis of satellite images from the years 2016, 2018, and 2019 using the MapBiomas platform. The results show changes in the predominant land cover classes over time, with an increase in the caatinga area and a decrease in the pasture area. An analysis of the vegetation indices (NDVI and LAI) indicated low vegetation cover and biomass in the study area, with a slight increase in the NDVI in 2018. An analysis of the Modified Normalized Difference Water Index (MNDWI) showed that the water content in the study area was generally low, with no significant variations over time. An increase in the water bodies, mainly due to the construction of a reservoir, was noted. The results of this study have provided important information for natural resource management in the region, including the development of strategies for the sustainable use and management of natural resources, particularly water resources, vegetation cover, and soil conservation. Full article

Air-assisted spraying is a commonly used spraying method for orchard plant protection operations. However, its spraying parameters have complex effects on droplet distribution. The lack of large-scale 3D droplet density distribution measurement methods of equipment has limited the optimization of spraying parameters. Therefore, there is a need to develop a method that can quickly obtain 3D droplet distribution. In this study, a 2D LiDAR was used to quickly scan moving droplets in the air, and a test method that can obtain the visualization of 3D droplet distribution was constructed by using the traveling mode of the machine perpendicular to the scanning plane. The 3D droplet distribution at different positions of the nozzle installed in the air-assisted system was tested at different fan rotation speeds, and the methods for signal processing, point cloud noise reduction, and point cloud division for 2D LiDAR were developed. The results showed that the LiDAR-based method for detecting 3D droplet distribution is feasible, fast, and environmentally friendly. Full article

The objective of the study was to examine the influence of moisture content on the mechanical properties of solid biomass, which is important for storage and handling. Mechanical properties involving powder flowability were determined with a Jenike shear tester. The materials tested were powdered biomass: sunflower husks, apple pomace, dried distillers grains with solubles (DDGS), and meat and bone meal. In static operations in which powdered biomass is generally under a significant load, such as in silos, moisture deteriorates the mechanical properties of biomass and increases its cohesion. In the case of DDGS, an additional slight decrease in stress was observed for samples with the highest moisture content, which was above 20%. For meat and bone meal and sunflower husks, a lubrication phenomenon was clearly observed, in which biomass samples with increased moisture content manifested better flowability, requiring lower boundary shear stresses than less moist samples. For apple pomace samples, with normal stresses above 50 kPa, the addition of moisture above 10% did not change the values of the observed shear stresses, indicating the stability of their properties. The use of powdered biomass with higher moisture contents, at above 10%, should be avoided, as such material can lead to overhangs in tanks and silos. Full article