AgriEngineering, Volume 5, Issue 3 (September 2023) – 31 articles

The survival of newly planted seedlings and their successful development after transplantation, including faster plant growth, improved plant quality, larger production, and the absence of dependence on arable land, is one of the primary goals of horticultural nurseries. Although peat is the most frequently used amendment in commercial potting substrates, exploiting it degrades essential ecosystems like peatlands and uses slowly renewable resources. This study evaluated the growth and nutrition of olive-rooted cuttings when peat was partially or completely replaced with vermicompost, searching for more sustainable methods and recovering urban wastewater treatment sludge sequentially. The progress of the plants’ growth was compared to that of corresponding plants in which commercial peat had been used as substrate. Leachates from every procedure were also examined, and the results revealed that trace element and heavy metal contents were much lower than those deemed hazardous for aquifers and soil. The outcomes indicated that peat might be effectively replaced with vermicompost sludge, promoting plant growth without further fertilizer. Comparatively to olive cuttings grown in peat-based substrates, those grown in compost-based substrates experienced improved nutrition and development. Further, it was found that irrigation doses were significantly reduced in treatments with a significant amount of vermicompost as the water drained more slowly. A technical-economic analysis was being conducted in the meantime, illustrating the financial benefits for a nursery when peat is replaced with vermicomposted sludge. Full article

The quantification of forage availability in tropical grasses is generally done in a destructive and time-consuming manner, involving cutting, weighing, and waiting for drying. To expedite this process, non-destructive methods can be used, such as unmanned aerial vehicles (UAVs) equipped with high-definition cameras, mobile device images, and the use of the normalized difference vegetation index (NDVI). However, these methods have been underutilized in tropical pastures. A literature review was conducted to present the current state of remote tools’ use in predicting forage availability and quality in tropical pastures. Few publications address the use of non-destructive methods to estimate forage availability in major tropical grasses (Megathyrsus maximus; Urochloa spp.). Additionally, these studies do not consider the fertility requirements of each cultivar and the effect of management on the phenotypic plasticity of tillers. To obtain accurate estimates of forage availability and properly manage pastures, it is necessary to integrate remote methods with in situ collection of soil parameters. This way, it will be possible to train machine learning models to obtain precise and reliable estimates of forage availability for domestic ruminant production. Full article

Open pit mining causes damage in natural and rural regions; that is why soil restoration is necessary in order to recovery soil–plant systems. The application of waste can be a good solution for rehabilitation, and it clearly complies with the circular economy and the zero-waste strategy. This study was carried out in a quarry restoration area in the southeast of Spain, where experimental plots were designed and fertilized with different amendments (commonly used inorganic fertilizer N-K-P, pig slurry, pruning waste and urban solid wastes) with the objective of studying ways to improve the restoration of the soil by using these residues and increase the soil fertility before planting. The treatments applied were evaluated in the short term (two and four months from their addition to topsoil) and medium term (nine months) in order to determine if the restored soils will be adequate for agriculture based on nutrients’ availability. The results showed that in all the treatments, the pH exceeded 8.5 due to the nature of the soil matrix, but after 9 months of the application, in the plots treated with NPK and pig slurry, the pH decreased. In general, with the application of the treatments, soil macro- (N, P, K, Na, Ca and Mg) and micro-nutrients (Fe and Cu) were increased. However, pig slurry and urban solid waste favored N and P, respectively. Full article

Tropical climate conditions favor landscape evolution and the formation of highly weathered soils under different pedogenic processes due to certain differential properties. Traditional analysis coupled with VNIR-SWIR reflectance spectroscopy and X-ray diffractometry (XRD) analyses can reveal such characteristics. Several researchers cited throughout this study already discussed the possible applications of analyses in this field. All agree that integrated knowledge (holistic) can drive the future of the soil sciences. However, few refer to the potential of soil spectroscopy in deriving pedogenetic information. Thus, this paper aimed to assess pedomorphogeological relationships in a representative toposequence of the Brazilian Midwest using traditional analyses and geotechnologies. We performed landscape observations and soil sampling in the field. The laboratory’s physical, chemical, spectral, and mineralogical determinations supported the soil classification according to the World Reference Basis (WRB/FAO) system. Based on the analysis results, we divided five profiles into two soil groups (highly and slightly weathered soils) using Pearson’s correlation and hierarchical clustering analysis (HCA). Traditional analyses determined the diagnostic attributes. Spectroscopic readings from 0.35 to 2.5 µm wavelengths and XRD supported identifying soil attributes and properties. Finally, all soil classes were correlated according to correspondent reflectance spectra and primary pedological attributes. There was a strong correlation between spectral oxide features and X-ray diffraction peaks. The HCA based on oxide content and mineral composition validated the previous soil grouping. Thus, we could assess the pedomorphogeological relationships through VNIR-SWIR spectroscopy, XRD, and traditional analyses concerning pedogenic processes through their correlation with soil properties resulting from these processes. However, periodic measurements are required, making orbital sensing a continuous data source for soil monitoring. Full article

Accurately identifying the boundaries of agricultural land is critical to the effective management of its resources. This includes the determination of property and land rights, the prevention of non-agricultural activities on agricultural land, and the effective management of natural resources. There are various methods for accurate boundary detection, including traditional measurement methods and remote sensing, and the choice of the best method depends on specific objectives and conditions. This paper proposes the use of convolutional neural networks (CNNs) as an efficient and effective tool for the automatic recognition of agricultural land boundaries. The objective of this research paper is to develop an automated method for the recognition of agricultural land boundaries using deep neural networks and Sentinel 2 multispectral imagery. The Buinsky district of the Republic of Tatarstan, Russia, which is known to be an agricultural region, was chosen for this study because of the importance of the accurate detection of its agricultural land boundaries. Linknet, a deep neural network architecture with skip connections between encoder and decoder, was used for semantic segmentation to extract arable land boundaries, and transfer learning using a pre-trained EfficientNetB3 model was used to improve performance. The Linknet + EfficientNetB3 combination for semantic segmentation achieved an accuracy of 86.3% and an f1 measure of 0.924 on the validation sample. The results showed a high degree of agreement between the predicted field boundaries and the expert-validated boundaries. According to the results, the advantages of the method include its speed, scalability, and ability to detect patterns outside the study area. It is planned to improve the method by using different neural network architectures and prior recognized land use classes. Full article

An ever-increasing amount of microplastics enters the environment and affects soil properties and plant growth. Investigating how the interactions between microplastics and soil properties vary across different soil types is crucial. In sandy soil, the subcritical SWR induced by microplastics may affect other soil properties. The objective of this study was to assess the impact of adding three types of microplastics (high-density polyethylene, polyvinyl chloride, and polystyrene) at a concentration of 5% (w/w) to sandy soil on the persistence and severity of SWR, as well as on various soil properties (bulk density, water sorptivity, and hydraulic conductivity) and plant characteristics (fresh and dry weight, maximum photochemical efficiency of PSII, and nutrient content) of radish (Raphanus sativus L.). It was found that microplastic contamination increased the persistence and severity of SWR and decreased soil bulk density, water sorptivity, and hydraulic conductivity. The total biomass measurements did not reveal a significant difference between the microplastic treatments and the control group. This study did not confirm any significant influence of microplastic contamination on the maximum photochemical efficiency of PSII, a measure of crop photosynthesis. Even though the value of photosynthetic efficiency changed with time, the values for all treatments stabilised at the end of the experiment. Microplastic contamination did not significantly alter crops’ nitrogen, phosphorus, potassium, or zinc contents. However, the copper content was reduced in all treatments, and magnesium and iron were reduced in the PVC and PS treatments compared to the control. The microplastic-induced changes in biomass or photosynthetic efficiency do not correspond to the changes in crop element concentrations. Full article

Using microorganisms to enhance crop productivity is an active and increasing field of research, which encompasses the productive, environmental, and economic aspects of agricultural production to obtain high-quality crops with a reduction in the need for fertilizers. Among the nutrients necessary for plant growth, phosphorous is problematic due to its low availability and its susceptibility to convert into non-labile forms. In this regard, phosphate-solubilizing bacteria (PSB) can be an interesting tool to improve phosphorous availability and to reduce the requirements of phosphate fertilizers. This work aimed to evaluate the potential use of phosphate-solubilizing bacteria in the supply of phosphorus and soybean development. This study was conducted in the 2019/2020 and 2020/2021 harvests. The experimental design was in randomized blocks, containing seven treatments and six replicates. The treatments consisted of five doses of phosphate fertilization, using triple superphosphate fertilizer, associated with the application of Bacillus subtilis and Bacillus megaterium bacteria, and two treatments, with and without the use of phosphorous fertilizer and without the use of an inoculant. Plant tissue nutrients and biometric and productive parameters of the crop were assessed. According to the observed results, applying PSB associated with phosphate fertilization and phosphate fertilization alone did not influence soybean’s nutritional, biometric, and productive parameters in the two harvests. Thus, the application of B. subtilis and B. megaterium, either associated or not associated with phosphate fertilization, does not contribute to the nutrition, development, and yield of soybean crops in soil with a naturally low P content, considering the climatic and soil conditions of the study. Full article

Detecting objects in digital images is challenging in computer vision, traditionally requiring manual threshold selection. However, object detection has improved significantly with convolutional neural networks (CNNs), and other advanced algorithms, like region-based convolutional neural networks (R-CNNs) and you only look once (YOLO). Deep learning methods have various applications in agriculture, including detecting pests, diseases, and fruit quality. We propose a lightweight YOLOv4-Tiny-based object detection system with a circular bounding box to accurately determine chrysanthemum flower harvest time. The proposed network in this study uses a circular bounding box to accurately classify the degree of chrysanthemums blooming and detect circular objects effectively, showing better results than the network with the traditional rectangular bounding box. The proposed network has excellent scalability and can be applied to recognize general objects in a circular form. Full article

The gin stand power is measurable using common tools; however, such tools typically do not detect active ginning. Detecting active ginning is important when trying to separate out the energy going to the moving parts of the gin stand (i.e., the baseline energy) versus the active energy doing work to remove the cotton fibers from the seed. Studies have shown that the gin stand is the second largest consumer of electricity in the ginning operation, while electricity accounts for nearly 17% of the average expense per bale. If active energy differences exist between cotton cultivars, there may be room to optimize and lower these expenses. The goal of the current work is to provide a method (and software tool) to analyze typical power logger data, and extract periods of active ginning, along with the energy consumed and ginning times, in a semi-automated way. The new method presented allows multiple periods of active ginning in a single file, and can separate the total energy into the active and baseline components. Other metrics of interest that the software calculates include the ginning time, and average power. Software validation using a simulated test signal showed that a 2%-or-lower error is possible with a noisy signal. Full article

The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations. Full article

This study investigated the potential and sustainable use of the biomass derived from various stages of the grape drying process. A total of eleven byproducts, each containing varying organic materials, were produced and subjected to testing. Ultimate analysis, as well as analyses of heating values, chemical composition, lignocellulose composition, total solids concentration and biogas production were performed with the recommended criteria and assessment methods. The results reveal that carbon (C), nitrogen (N), hydrogen (H), and oxygen (O) levels were significantly different among the byproducts. The ash content of byproducts 5–11 ranged from 3.56 to 5.11%, which was lower than the estimated values in the other byproducts. The analysis of higher heating value showed significantly higher calorific values for byproducts 10 and 11 (22.73 ± 0.08 and 22.80 ± 0.07 MJ kg⁻¹, respectively). Byproducts 1–9 had lower sugar content than byproducts 10 and 11 (rejected raisins). Byproducts 5–9 had the lowest lignin content, and there were no significant differences in neutral detergent fiber (NDF) contents between byproducts 1–6. The highest accumulated biogas volume after 40 days was 11.50 NL L⁻¹ of substrate for byproduct group C (byproducts 10 to 11), followed by 11.20 NL L⁻¹ of substrate for byproduct group B (byproducts 5–9) and 9.51 NL L⁻¹ of substrate for byproduct group A (byproducts 1–4). It is concluded that byproducts consisting of biomass derived at different stages of raisin production may be an effective solid fuel and energy source. The amounts of volatile solids in the tested raisin processing byproducts indicated their appropriateness for pyrolysis conversion to a liquid product with high volatile content. Full article

The aim of this work was to elaborate a new methodology that can allow for the identification of the topsoil homogeneous area (tSHA) distribution along land parcels, supporting farmers in keeping low-cost, sustainable, and light logistic management of precision agriculture (PA) practices. This paper shows the assessment of tSHA variability over two production units (PUs), considering radiometric response (optical camera), physicochemical (texture, pH, electrical conductivity), and statistical and geostatistical data analysis. By using unmanned aircraft systems (UASs) and laboratory analysis, our results revealed that the integration between UAS-RGB and physicochemical data can improve the estimation accuracy of tSHA distribution. Firstly, the UAS-RGB dataset was used to isolate bare soil from the vegetative radiometric contribution. Secondly, starting from statistical approaches (correlation matrices), the highest correlation with UAS-RGB and physicochemical data was stated. Thirdly, by using a geostatistical approach (ordinary cokriging), the map representing the tSHA variability was finally obtained. Full article

Shrimp farming and exporting is the main income source for the southern coastal districts of the Mekong Delta. Monitoring these shrimp ponds is helpful in identifying losses incurred due to natural calamities like floods, sources of water pollution by chemicals used in shrimp farming, and changes in the area of cultivation with an increase in demand for shrimp production. Satellite imagery, which is consistent with good spatial resolution and helpful in providing frequent information with temporal imagery, is a better solution for monitoring these shrimp ponds remotely for a larger spatial extent. The shrimp ponds of Cai Doi Vam township, Ca Mau Province, Viet Nam, were mapped using DMC-3 (TripleSat) and Jilin-1 high-resolution satellite imagery for the years 2019 and 2022. The 3 m spatial resolution shrimp pond extent product showed an overall accuracy of 87.5%, with a producer’s accuracy of 90.91% (errors of omission = 11.09%) and a user’s accuracy of 90.91% (errors of commission = 11.09%) for the shrimp pond class. It was noted that 66 ha of shrimp ponds in 2019 were observed to be dry in 2022, and 39 ha of other ponds had been converted into shrimp ponds in 2022. The continuous monitoring of shrimp ponds helps achieve sustainable aquaculture and acts as crucial input for the decision makers for any interventions. Full article

Timely detection of pests and diseases in crops is essential to mitigate severe damage and economic losses, especially in the context of climate change. This paper describes a method for detecting the presence of coffee leaf rust (CLR) using two databases: RoCoLe and a database obtained from an unmanned aerial vehicle (UAV) equipped with an RGB camera. The developed method follows a two-stage approach. In the first stage, images are processed using ImageJ software, while, in the second phase, Python is used to implement morphological filters and the Hough transform for rust identification. The algorithm’s performance is evaluated using the chi-square test, and its discriminatory capacity is assessed through the generation of a Receiver Operating Characteristic (ROC) curve. Additionally, Cohen’s kappa method is used to assess the agreement among observers, while Kendall’s rank correlation coefficient (KRCC) measures the correlation between the criteria of the observers and the classifications generated by the method. The results demonstrate that the developed method achieved an efficiency of 97% in detecting coffee rust in the RoCoLe dataset and over 93.5% in UAV images. These findings suggest that the developed method has the potential to be implemented in the future on a UAV for rust detection. Full article

To address the challenges of climate change and food security, the establishment of smart farm complexes is necessary. While there have been numerous studies on the productivity and environmental control of individual greenhouses, research on greenhouse complexes is considerably limited. Conducting environmental studies during the design phase of these complexes poses financial constraints and practical limitations in terms of on-site experiments. To identify potential issues that may arise when developing large-scale greenhouse complexes, it is possible to utilize modeling techniques using computational fluid dynamics (CFD) to assess environmental concerns and location issues before constructing the facilities. Consequently, simulating large-scale CFD models that incorporate multiple greenhouses and atmospheric conditions simultaneously presents significant numerical challenges. The objective of this study was to design and verify the 3D CFD model for a large-scale Venlo greenhouse, where acquiring field data before construction is not feasible for designing a greenhouse complex. The verification of the CFD models was conducted using the improved grid independence test (GIT) and wall Y⁺ approaches. The findings revealed that a grid resolution of 0.8 m and a first-layer height of 0.04 m were suitable for developing large-scale greenhouse models, resulting in a low Root Mean Square Error (RMSE) of 3.9% and a high coefficient of determination (R²) of 0.968. This process led to a significant reduction of 38% in the number of grid cells. Subsequently, the aerodynamic characteristics and regional ventilation efficiency were analyzed in a 3D greenhouse model for developing a new large-scale greenhouse complex. Full article

Many farms utilize closed-type livestock systems to enhance productivity and facilitate effective environmental management. However, the confined nature of these closed spaces poses an increased risk of exposure to harmful gases and organic dust for both workers and livestock. Additionally, the introduction of outside air through ventilation systems can lead to temperature fluctuations within the breeding environment, resulting in potential productivity issues. This research paper employs computational fluid dynamics (CFD) to develop ventilation operation management plans that address both the working environment and the breeding environment simultaneously. The proposed plans are designed to be easily implemented in practical farm settings. The findings of this study, based on the simulation analysis, indicate that while ventilation is effective in reducing harmful gases and improving the working environment, its efficiency decreases after the initial 3 min of operation. Furthermore, uncontrolled ventilation can cause sudden temperature changes, which may adversely affect the well-being of the livestock. However, when upgraded ventilation structures are implemented, significant improvements in the working environment (an average of 27.3% improvement) can be achieved while maintaining temperature stability for the livestock. These results highlight the importance of referring to the provided ventilation operation management table before commencing work, as it enables workers to improve the working environment while minimizing the potential impact of ventilation on the breeding environment. Full article

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