AgriEngineering, Volume 6, Issue 2 (June 2024) – 61 articles

Animal welfare strongly influences the health and performance of cattle and is an important factor for consumer acceptance. One parameter for the quantification of health status is the lying duration, which can be deployed for the early detection of possible production-related illnesses. Usually, 3D-accelerometers are the tool to detect lying duration in cattle, but the handling of bulls sometimes has special requirements because frequent manipulation in daily farming routines is often not possible. An ultrahigh-frequency (UHF) radio-frequency identification (RFID) system was installed in a beef cattle barn in Germany to measure the activity and lying time of bulls. Such UHF RFID systems are typically used for estrus detection in dairy cows via activity level, but can also be considered, for instance, as an early detection for lameness or other diseases. The aim of the study was to determine whether the estimations of activity level and lying duration can also be traced in husbandry systems for fattening bulls. Two groups of bulls (Uckermärker cattle, n = 10 and n = 13) of the same age were equipped with passive UHF RFID ear transponders. Three cameras were installed to proof the system and to observe the behaviour of the animals (standing, lying, and moving). Furthermore, accelerometers were attached to the hind legs of the bulls to validate their activity and lying durations measured by the RFID system in the recorded area. Over a period of 20 days, position (UHF RFID) and accelerometer data were recorded. Videos were recorded over a period of five days. The UHF RFID system showed an overall specificity of 95.9%, a sensitivity of 97.05%, and an accuracy of 98.45%. However, the comparison of the RFID and accelerometer data revealed residuals (ԑ) of median lying time (in minutes per day) for each group of ԑGroup1 = 51.78 min/d (p < 0.001), ԑGroup2 = −120.63 min/d (p < 0.001), and ԑGroup1+2 = −34.43 min/d (p < 0.001). In conclusion, UHF RFID systems can provide reliable activity and lying durations in 60 min intervals, but accelerometer data are more accurate. Full article

Tillage, as a key agricultural operation, has an important influence on soil properties and crop productivity. However, tillage at the same depth is not always the best choice as differences in soil texture, compacted topsoil, or plow pan at different depths, crop rotation, and root penetration potential signal that the depth of tillage should take greater account of the factors involved. Variable depth tillage (VDT) is an important precision farming operation, linking soil, plants, tillage machinery, smart sensors, measuring devices, computer programs, algorithms, and variability maps. This topic is important from an agronomic, energy, and environmental perspective. However, the application of VDTs in practice is currently still very limited. The aim of this study was to carry out a detailed review of scientific work on variable depth tillage, highlighting the importance of soil compaction and VDT; the measurement methods and equipment used; and the impact on soil, crops, the environment, and the economy. Based on the reviewed studies, there is a lack of studies that use fully automated depth control of tillage systems based on input data obtained with on-the-go (also known as online) proximal soil sensing. In precision agriculture, rapidly developing Internet of Things technologies allow the adaptation of various farming operations—including tillage depth—to site-specific and temporal conditions. In this context, the use of proximal soil sensing technologies coupled with electromagnetic induction, gamma rays, and multi-sensor data fusion to provide input for recommended tillage depth would be beneficial in the future. The application of VTD in specific areas is promising as it helps to reduce the negative effects of soil compaction and avoid unnecessary use of this expensive and environmentally damaging technological operation. Full article

In the last decade, Smart Glasses (SG) and augmented reality (AR) technology have gained considerable interest in all production sectors. In the agricultural field, an SG can be considered a valuable device to support farmers and agricultural operators. SGs can be distinguished by technical specification, type of display, interaction system, and specific features. These aspects can affect their integration into farms, influencing users’ experience and the consequent level of performance. The aim of the study was to compare four SGs for AR with different technical characteristics to evaluate their potential integration in agricultural systems. This study analyzed the capability of QR code reading in terms of distance and time of visualization, the audio–video quality of image streaming during conference calls and, finally, the battery life. The results showed different levels of performance in QR code reading for the selected devices, while the audio–video quality in conference calls demonstrated similar results for all the devices. Moreover, the battery life of the SGs ranged from 2 to 7 h per charge cycle, and it was influenced by the type of usage. The findings also underlined the potential use and integration of SGs to support operators during farm management. Specifically, SGs might enable farmers to obtain fast and precise augmented information using markers placed at different points on the farm. In conclusion, the study highlights how the different technical characteristics of SG represent an important factor in the selection of the most appropriate device for a farm. Full article

This study assesses the impact of defoliation applied to three developmental stages across three cropping seasons from 2021 to 2023 on growth, yield and forage quality in maize. The experimental design included three treatments: defoliation of three expanded leaves at the 3rd–4th leaf stage (DF1), the 5th–6th expanded leaves by leaf punch (DF2) and expanding leaves with the DF2 treatment (DF3) at the 6th–7th leaf stages, compared with no defoliation (control). Over three years, the most significant decrease in dry matter (DM) yield occurred in DF1 during spring sowing, while in summer sowing, the largest reduction was in DF3, both of which were correlated with changes in the number of grains per ear. The DM yields at harvest were positively correlated with plant leaf areas at the silking stage. The digestibility of forage in in vitro DM decreased concomitantly with an increase in acid detergent fiber content, indicating a decrease in forage quality. Given the frequent severe damage observed in summer sown maize and the detrimental effects of early growth stage leaf feeding on quality and quantity of spring sown maize, the application of registered insecticides is advised to reduce pest damage to maize crops. Full article

This study investigated the interoperability of a tomato fruit detection model trained using nighttime images from two greenhouses. The goal was to evaluate the performance of the models in different environmets, including different facilities, cultivation methods, and imaging times. An innovative imaging approach is introduced to eliminate the background, highlight the target plants, and test the adaptability of the model under diverse conditions. The results demonstrate that the tomato fruit detection accuracy improves when the domain of the training dataset contains the test environment. The quantitative results showed high interoperability, achieving an average accuracy (AP₅₀) of 0.973 in the same greenhouse and a stable performance of 0.962 in another greenhouse. The imaging approach controlled the lighting conditions, effectively eliminating the domain-shift problem. However, training on a dataset with low diversity or inferring plant appearance images but not on the training dataset decreased the average accuracy to approximately 0.80, revealing the need for new approaches to overcome fruit occlusion. Importantly, these findings have practical implications for the application of automated tomato fruit set monitoring systems in greenhouses to enhance agricultural efficiency and productivity. Full article

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. The Fruit Harvest Helper seeks to simplify their process by detecting apples on images of apple trees. Once the number of apples is detected, a correlation can then be applied to this value to obtain a usable yield estimate for an apple tree. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. Borrowing ideas from the former iOS app, the new application was designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app uses a color ratio-based image-segmentation algorithm written in C++ to detect apples. This algorithm detects apples within apple tree images that farmers select for processing by using OpenCV functions and C++ code. The results of testing the algorithm on a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. The algorithm’s processing time was tested for Android and iOS, yielding an average performance of 1.16 s on Android and 0.14 s on iOS. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture. The app is nearing readiness for farmers to use for the purpose of yield monitoring and farm management within Pink Lady apple orchards. Full article

Soil compaction between crop rows can increase a machine’s performance by reducing rolling resistance and fuel demand. Controlled Traffic Farm (CTF) stands out among modern techniques for increasing agricultural sustainability because the machines continuously travel along the same path in the field, reducing plant crush and compacting the soil in the traffic line. This study evaluated fuel consumption and CO₂ emissions at different CTF intensities in different soil management strategies for soybean crop. The experimental design involved randomized blocks in a split-plot scheme with four replications. The plots constituted the three types of soil management: conventional tillage, no-tillage with straw millet cover, and no-tillage with brachiária straw cover. The subplots constituted for agricultural tractors were passed over in traffic lines (2, 4, and 8 times). We evaluated agricultural tractor fuel consumption, CO₂ emissions, and soybean productivity. The straw cover and tractor-pass significantly affected the fuel consumption and greenhouse gas emissions of the soybean cultivation. Fuel consumption and CO₂ emissions were reduced due to the machine-pass increase, regardless of soil management. Thus, a CTF reduces rolling resistance and increases crop environmental efficiency. Bare-soil areas increased by 20.8% and 27.9% with respect to fuel consumption, compared to straw-cover systems. Brachiária straw and millet reduce CO₂ emissions per hectare by 20% and 28% compared to bare soil. Lower traffic intensities (two passes) showed (13.72%) higher soybean yields (of 4.04 Mg ha⁻¹). Investigating these effects in other types of soil and mechanized operations then becomes essential. Full article

Several factors contribute to the increase in irrigation demand: population growth, demand for higher value-added products, and the impacts of climate change, among others. High-quality water is essential for irrigation, so knowledge of water quality is critical. Additionally, water use in agriculture has been increasing in the last decades. Lack of water quality can cause drip clog, a lack of application uniformity, cross-contamination, and direct and indirect impacts on plants and soil. Currently, there is a need for more automated methods for evaluating and monitoring water quality for irrigation purposes, considering different aspects, from impacts on soil to impacts on irrigation systems. This work proposes a data-driven method to address this gap and implemented it in a case study in the PCJ river basin in Brazil. The methodology contains nine components and considers the main steps of the data lifecycle and the traditional machine learning workflow, allowing for automated knowledge extraction and providing important information for improving decision making. The case study illustrates the use of the methodology, highlighting its main advantages and challenges. Clustering different scenarios in three hydrological years (high, average, and lower streamflows) and considering different inputs (soil-related metrics, irrigation system-related metrics, and all metrics) helped generate new insights into the area that would not be easily obtained using traditional methods. Full article

Among the technological tools used in precision agriculture, the convolutional neural network (CNN) has shown promise in determining the nutritional status of plants, reducing the time required to obtain results and optimizing the variable application rates of fertilizers. Not knowing the appropriate amount of nitrogen to apply can cause environmental damage and increase production costs; thus, technological tools are required that identify the plant’s real nutritional demands, and that are subject to evaluation and improvement, considering the variability of agricultural environments. The objective of this study was to evaluate and compare the performance of two convolutional neural networks in classifying leaf nitrogen in strawberry plants by using RGB images. The experiment was carried out in randomized blocks with three treatments (T1: 50%, T2: 100%, and T3: 150% of recommended nitrogen fertilization), two plots and five replications. The leaves were collected in the phenological phase of floral induction and digitized on a flatbed scanner; this was followed by processing and analysis of the models. ResNet-50 proved to be superior compared to the personalized CNN, achieving accuracy rates of 78% and 48% and AUC of 76%, respectively, increasing classification accuracy by 38.5%. The importance of this technique in different cultures and environments is highlighted to consolidate this approach. Full article

The poultry industry is shifting towards more sustainable and ethical practices, including adopting cage-free (CF) housing to enhance hen behavior and welfare. However, ensuring optimal indoor air quality, particularly concerning particulate matter (PM), remains challenging in CF environments. This study explores the effectiveness of electrostatic particle ionization (EPI) technology in mitigating PM in CF hen houses while considering the height at which the technology is placed and the duration of the electric supply. The primary objectives are to analyze the impact of EPI in reducing PM and investigate its power consumption correlation with electric supply duration. The study was conducted in a laying hen facility with four identical rooms housing 720 laying hens. The study utilized a Latin Square Design method in two experiments to assess the impact of EPI height and electric supply durations on PM levels and electricity consumption. Experiment 1 tested four EPI heights: H1 (1.5 m or 5 ft), H2 (1.8 m or 6 ft), H3 (2.1 m or 7 ft), and H4 (2.4 m or 8 ft). Experiment 2 examined four electric supply durations: D1 (control), D2 (8 h), D3 (16 h), and D4 (24 h), through 32 feet corona pipes. Particulate matter levels were measured at three different locations within the rooms for a month, and statistical analysis was conducted using ANOVA with a significance level of ≤0.05. The study found no significant differences in PM concentrations among different EPI heights (p > 0.05). However, the duration of EPI system operation had significant effects on PM₁, PM_2.5, and PM₄ concentrations (p < 0.05). Longer EPI durations resulted in more substantial reductions: D2—17.8% for PM₁, 11.0% for PM_2.5, 23.1% for PM4, 23.7% for PM₁₀, and 22.7% for TSP; D3—37.6% for PM₁, 30.4% for PM_2.5, 39.7% for PM₄, 40.2% for PM₁₀, and 41.1% for TSP; D4—36.6% for PM₁, 24.9% for PM_2.5, 38.6% for PM₄, 36.3% for PM₁₀, and 37.9% for TSP compared to the D1. These findings highlight the importance of prolonged EPI system operation for enhancing PM reduction in CF hen houses. However, utilizing 16 h EPI systems during daylight may offer a more energy-efficient approach while maintaining effective PM reduction. Further research is needed to optimize PM reduction strategies, considering factors like animal activities, to improve air quality and environmental protection in CF hen houses. Full article

The three-point hitch, found on agricultural tractors, facilitates the raising and lowering of an attached implement. Some tractors include a rock shaft that comprises a physical shaft that interconnects and facilitates the raising and lowering of the lower arms of the three-point hitch in a synchronized manner. In this study, we deal with a hitch system with the lower arms actuated by two independent hydraulic cylinders. This innovative tractor hitch system design allows the implement to follow the terrain, instead of the tractor, about the fore–aft (roll) axis of the tractor. However, since the two lower arms are independent, a specialized controller is needed to move these arms in unison. First, we present a position controller for individual arms and a roll controller to move these arms together. Second, we present a unique algorithm to emulate a physical rock shaft while the implement is operating in float mode. The algorithm ensures that the implement does not roll around the fore–aft axis while making sure it moves up and down vertically to follow the terrain. We present experimental results from the step response of the hitch system’s height while tracking a velocity reference. With the roll of the implement defined as the difference between the left arm’s position in percentage and that of the right arm in percentage, we observe that the largest mean roll was 0.23% with a flail mower attached and 0.26% without any implement. We then present results from the implement’s position in the float mode when the software rock shaft was activated and compare them with the case without the software rock shaft. The experiments showed that, when the software rock shaft was turned on, the mean roll reduced from 4.64% to 0.58% with a seed drill implement and from −3.99% to −0.59% with a flail mower implement. The standard deviations in these two implement cases improved from 16.77% to 2.79% and 6.45% to 3.53%, respectively, proving the effectiveness of the software rock shaft and its potential to replace the physical rock shaft found on the traditional tractors. Full article

This study aimed to determine the usage potential of vacuum-dehydrated ground beef in beef patty production. First, the fresh ground beef was dehydrated in vacuum dryers at 25, 35, and 45 °C for dehydration kinetics and color change. Then, the vacuum-dehydrated ground beef was rehydrated, and three different beef patties were separately produced using fresh ground beef, the rehydrated ground beef, and a mixture of the two (1:1). According to the results, the dehydration significantly decreased the L*, a*, and b* values of ground beef; however, after rehydration, the L* and b* values were not significantly different from the control values. The cooking loss for beef patties produced with rehydrated ground beef was higher than the control. However, there was no significant difference in the sensory of the beef patties among the treatments. In conclusion, there is potential for using vacuum-dehydrated ground beef in beef patty production. Full article

The coffee leaf miner (Leucoptera coffeella) is a key pest in coffee-producing regions in Brazil. The objective of this work was to evaluate the potential of machine learning algorithms to identify coffee leaf miner infestation by considering the assessment period and Sentinel-2 satellite images generated on the Google Earth Engine platform. Coffee leaf miner infestation in the field was measured monthly from 2019 to 2023. Images were selected from the Sentinel-2 satellite to determine 13 vegetative indices. The selection of images and calculations of the vegetation indices were carried out using the Google Earth Engine platform. A database was generated with information on coffee leaf miner infestation, vegetation indices, and assessment times. The database was separated into training data and testing data. Nine machine learning algorithms were used, including Linear Discriminant Analysis, Random Forest, Support Vector Machine, k-nearest neighbors, and Logistic Regression, and a principal component analysis was conducted for each algorithm. After optimizing the hyperparameters, the testing data were used to validate the model. The best model to estimate miner infestation was RF, which had an accuracy of 0.86, a kappa index of 0.64, and a precision of 0.87. The developed models were capable of monitoring coffee leaf miner infestation. Full article

Vortex generators (VGs) attached to the leading edge of an agricultural aircraft are purported to control airflow over the upper surface of the wing by creating small vortices that delay boundary layer separation, thereby improving the performance of the aircraft. These devices are commercially available for use in the aviation industry, primarily to increase pilot control of the aircraft. The benefits attributed to VGs remain largely descriptive and anecdotal in nature without rigorous empirical assessment in the field. The intent of this study was to evaluate whether this aerodynamic device could improve deposition or reduce drift when mounted on an agricultural aircraft. Airborne drift and ground deposition were measured with monofilament lines and Mylar cards, respectively. Deposits were expressed as percent of fluorometric response using a spectrofluorophotometer. There were 46% fewer downwind drift deposits on monofilament lines when VGs were installed than when VGs were not installed. Whether or not VGs were installed on the aircraft was the predominant factor which influenced deposition on monofilament lines. Spray deposits on Mylar cards placed at ground level downwind of the applications at three different locations (5, 10, and 20 m) varied significantly (p < 0.0001) between treatments, with corresponding 31, 54, and 61% reductions in downwind deposits when VGs were installed. While these findings overall are positive, this is the first known study of its type, and more research is warranted to better understand the role of vortex generators in the reduction in drift relative to aerially applied sprays. Full article

Chlorella vulgaris and Scenedesmus sp. are commonly used in wastewater treatment due to their fast growth rates and ability to tolerate a range of environmental conditions. This study explored the cultivation of Chlorella vulgaris and Scenedesmus sp. using wastewater from the food industry, particularly from Italian basil pesto production tanks. The experiment involved different carbon dioxide concentrations and light conditions with a dilution rate of basil pesto wastewater at 1:2. Both microalgae strains were able to grow on pesto wastewater, and biomass characterization highlighted the influence of CO₂ supply and light irradiation. The highest lipid storage was 79.3 ± 11.4 mg g_{dry biomass}⁻¹ and 75.5 ± 13.3 mg g_{dry biomass}⁻¹ for C. vulgaris and S. obliquus under red light (5% CO₂ supply) and white light (0.04% CO₂ supply), respectively. Protein storage was detected at 20.3 ± 1.0% and 24.8 ± 1.3% in C. vulgaris and S. obliquus biomasses under white light with a 5% CO₂ and 0.04% CO₂ supply, respectively. The removal of P, N, chemical oxygen demand, and biological oxygen demand resulted in 80–100%, 75–100%, 26–35%, and 0–20%, respectively. Full article

The application of digital technologies in the agricultural sector is increasing. One of the new key technologies is the Internet of Things (IoT), which can facilitate the everyday work of farmers. For the successful adoption of IoT-enabled digital products and to ensure improved workflows, the usability of human–machine interfaces is highly important. Various design approaches of human–machine interfaces (HMIs) can currently be found in the monitoring of agricultural machinery. In this work, the most well-known HMIs are considered. Based on a usability test (participants n = 9), the user interface (UI) of a novel mobile application (NEVONEX Cockpit App) was chosen as an example of a design approach of an IoT ecosystem that is oriented towards the UI design of mobile applications. This work aims to identify the weak points of this UI. Conclusions about the needs, and thus an improvement of the user experience, are based on the suggestions for improvement and the information about the general requirements of farmers for a UI for agricultural machinery. Here, it was found that most farmers are satisfied with the UI design of their familiar tractor monitors. According to the survey, the three most important points to be considered in the UI design are intuitive operation and menu navigation, easy access to the essential functions and buttons, and sufficiently large control panels. The conducted usability tests can be considered a basis for developing HMIs for agriculture machinery. Through repeated execution of comparable usability tests, there is the possibility of developing future HMI guidelines for agriculture to improve the user experience (UX). For the NEVONEX ecosystem, feedback from the user interface testing was incorporated in a major revision of the Cockpit App’s design, where a lot more display space was given to the agronomic digital services by smartly arranging infrastructure functions in tiles. Full article

Saffron cultivation is important in global agriculture and is mainly flourishing in Mediterranean climates. Although it originated in Asia Minor, it is widely grown in regions such as Iran, India, Spain, Morocco, Greece, and Italy. Labour-intensive harvesting, mainly by hand, characterises its production and offers substantial employment opportunities in cultivating areas. However, traditional saffron-producing countries such as Spain, Italy, and Greece have witnessed declining production due to labour demands and competition from low-wage countries. Mechanization remains unfeasible due to the delicate nature of the plant. To revitalise saffron cultivation, efforts have been focused on reducing labour costs, improving productivity, and improving quality through innovative techniques, such as soilless crops. In this study, the productivity and quality of saffron was evaluated in a soilless culture and three fertigation doses were evaluated: a control, consisting of Sonneveld and Voogt’s standard nutrient solution, and two treatments with two supplemented solutions, 30% K and 30% Ca. The results indicated that the solution with 30% K obtained higher corm productivity, as well as better quality saffron, as all the products of this treatment were included in Category I according to the ISO 3632 standard, while the quality of saffron grown with the control solution was lower. Full article

The application of pesticides using unmanned aerial vehicles (UAVs) has grown, but there is a lack of information to support more efficient applications. Using a DJI AGRAS-MG-1P octocopter equipped with different spray tips, this study sought to explore spray deposition (leaves and fruit) and efficacy of chlorpyrifos on control of coffee berry borer at different spray volumes and flight heights. The study was conducted in an Arabica coffee plantation. The study consisted of eight treatments and four replications in a 2 × 2 × 2 factorial scheme: two flight heights (1.5 and 3.0 m), two spray tips (hollow cone and flat fan), and two spray volumes (10 and 15 L ha⁻¹). Deposition was assessed by detecting a tracer in the coffee leaves and fruit using spectrophotometry. The coffee berry borer-control efficacy trial was conducted in two areas by evaluating the percentage of damaged fruit 60 days after two insecticide applications. The flight height of 1.5 m promoted higher spray deposition on leaves and fruit and a lower incidence of damaged fruit. Flat fan spray tips resulted in higher spray deposition on the leaves, not interfering with the deposition on fruit or the coffee berry borer control. Increasing the spray volume from 10 to 15 L ha⁻¹ did not increase spray deposition on coffee leaves and fruit. Chlorpyrifos applied via UAVs reduced the incidence of coffee berry borer. Full article

Wood fuel from the agroforestry sector is one of the main strategies cited by the EU for reducing energetic dependance on foreign markets. Its sustainability, both economic and environmental, can be improved through the optimization of harvesting and chipping operations. This study was focused on the dynamic and energetic balance of the chipping phase carried out by a chipper operated by the power-take-off (PTO) of a medium-power tractor. Both machines were equipped with sensors for real-time monitoring of fuel consumption, PTO torque and speed, trunk diameter and working time during the comminution of 61 poplar trees grown in a medium rotation coppice system. The data analysis was carried out on the entire dataset (about 29,000 records) without considering their belonging to different trees. By means of proper data ordinations, it has been possible to define all the intervals in which the chipping stopped (e.g., between two trees) and to exclude them from the intervals of actual chipping. This has allowed forcomputation of operative and actual working time, as well as of the basic power required to operate the chipper and the power for actual chipping. Subsequently, the parameter values observed during actual chipping were related to the cutting diameters measured at the same instant. Subsequently, the dataset was divided according to seven diameter classes, and, for each class, the descriptive statistical indices of working time, work productivity, CO₂ emissions, energy requirement and fuel consumption were calculated. Eventually, the correlation between the variations in trunk diameter and other parameters was verified both on the whole dataset and based on the class average values. The analysis made it possible to identify the conditions of greatest efficiency for the chipper. More generally, the method could help to increase the accuracy of measurements aimed at characterizing the performance of chippers from the point of view of dynamic energy requirements as well as in relation to different wood species. Full article

The determination of water requirements for crops holds a crucial role in optimizing irrigation and enhancing agricultural productivity. However, identifying these needs remains a significant challenge due to the variety of factors influencing this decision, such as meteorological conditions, soil structure, and the phenological stages of each crop. In this study, we propose the design and development of a dedicated web-based irrigation advisory platform for arboriculture named ‘Soqia-Advice’. This platform will provide services to farmers, advisors, and decision-makers. The proposed methodology is based on four main steps: (1) need assessments; (2) definition of functionalities to fulfill these needs; (3) design of the overall architecture and the conceptual data model; and (4) implementation of key features of the module dedicated to farmers. The prototype of the “Farmer” module was tested on a farm in Azrou city, Morocco, as a case study. Seven-day weather forecasts were seamlessly integrated using the Weatherbit API. Additionally, the irrigation schedule was accurately displayed, ensuring efficient water management. Functionality tests were conducted on each menu to ensure the seamless and reliable operation of all planned features. The results were rigorously assessed to ensure that each feature aligned with the identified needs. Full article

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field. Full article

This study examined using peanut shells, rice husks, and cocoa husks as soil conditioners to boost yields in Allium cepa var. Alvara onions. Three types of biochar and four application rates (1%, 1.5%, 3%, and 5%) were compared to a control with no biochar. The biochars had different nutrient makeups, with cocoa husk biochar (CHB) containing the most essential elements. While overall plant growth (height, leaves, and roots) was not significantly affected (p > 0.05) by any biochar type compared to the control, some plant parts responded differently. CHB (5%) and peanut husk biochar (PHB) (1%) yielded the tallest onion plants (71 and 65 cm), while 1% rice and cocoa biochar resulted in the shortest (below 42 cm). PHB (3% and 5%) produced the longest roots (9 cm), while 1.5% rice husk biochar (RHB) had the shortest. Biochar application had no significant effect on leaf count. However, specific application rates of RHB and PHB increased the harvest index (HI), indicating more efficient yield allocation. HI values > 0.85 were obtained with specific biochar rates (e.g., 1.0–1.5% PHB, 1.5–5% RHB, or 5.0% CHB). Full article

Between 20 and 30% of the feedstock delivered cost is the highway hauling. In order to achieve maximum truck productivity, and thus minimize hauling cost, the hauling technology needs to provide for rapid loading and unloading. Three prototype technologies have been proposed to address the hauling issue. The first was developed by Stinger to secure a load of large rectangular bales, and it is identified as the Advanced Load Securing System (ALSS). For this study, the ALSS technology is applied on two trailers hooked in tandem (ALSS-2) loaded with 20 bales each. The second technology (Cable), is a cable system for securing a load of bales (round or rectangular) on a standard flatbed trailer. With the third technology (Rack), bales are loaded into a 20-bale rack at an SSL, and this rack is unloaded as a unit at the biorefinery. Bales remain in the rack until processed, thus avoiding single-bale handling at the receiving facility. A cost comparison, which begins with bales in single-layer ambient storage in SSLs and ends with bales in single file on a conveyor into the biorefinery, was done for the three hauling technologies paired with three load-out technologies. Cost for the nine options ranged from 48.56 USD/Mg (11 load-outs, Cable hauling) to 34.90 USD/Mg (8 loads-outs, ALSS-2 hauling). The most significant cost issue was the reduction in truck cost; 25.54 USD/Mg (20 trucks, Cable) and 15.15 USD/Mg (10 trucks, Rack). Full article

Computational fluid dynamics (CFD) may aid the design of barn ventilation systems by simulating indoor cattle thermal welfare. In the literature, CFD models of mechanically and naturally ventilated barns are proposed separately. Hybrid ventilation relies on cross effects between air change mechanisms that cannot be studied using existing models. The objective of this study was to develop a CFD methodology for modelling animal thermal comfort in hybrid ventilated barns. To check the capability of CFD as a design evaluation tool, a real case study (with exhaust blowers) and an alternative roof layout (with ridge gaps) were simulated in summer and winter weather. Typical phenomena of natural and mechanical ventilation were considered: buoyancy, solar radiation, and wind together with high-speed fans and exhaust blowers. Cattle thermal load was determined from a daily animal energy balance, and the assessment of thermal welfare was performed using thermohygrometric indexes. Results highlight that the current ventilation layout ensures adequate thermal welfare on average, despite large nonuniformity between stalls. The predicted intensity of heat stress was successfully compared with experimental measurements of heavy breathing duration. Results show strong interactions between natural and mechanical ventilation, underlining the need for an integrated simulation methodology. Full article

Garlic cultivation in tropical regions, such as the Brazilian Cerrado, faces the problem of secondary growth in the field induced by climatic conditions, which affects bulb quality and value. Clove essential oil (CEO) contains high levels of eugenol, which has the potential as an eco-friendly plant growth retardant (PGR) capable of reducing or inhibiting the secondary growth of bulbs in garlic cultivation. In this study, field experiments were carried out in two consecutive years (winter 2021 and 2022), spraying garlic plants with different concentrations of emulsion of CEO (0.0, 0.2, and 0.4%) in the differentiation phase; for comparison, the effects of water deficit, a prevalent agricultural technique in the region, were also evaluated. At a dose of 0.4%, the CEO reduced the prevalence of secondary growth and split bulbs without affecting yield. The mode of action of PGR was investigated by analyzing photosynthetic, enzymatic, and metabolomic parameters. The plants reduced amylolytic activity, and the photosynthetic parameters, after 7 days, were restored in both treatments. The analysis of the metabolomic profile of garlic leaves revealed changes in the pathways associated with the biosynthesis of fatty acids, wax, cutin, and suberin in plants treated with CEO, indicating possible damage to the surface coating of the leaf. Full article

Herbivore-induced plant organic volatiles (HIPVs) have recently been studied to improve biological pest control. In contrast, the effects of volatile organic compounds (VOCs) that are not induced by herbivory (non-HIPVs) have received less attention. The latter are essential in the first stages of crop colonization by entomophagous insects (predators and parasitoids) used in biological pest control programs. Furthermore, the effects on entomophagous insects of different cultivars of a cultivated botanical species have not been studied. The aim of this work was to study the different non-HIPVs found in 10 tomato cultivars used in tomato greenhouses on two entomophages: the egg parasitoid Trichogramma achaeae (Hymenoptera, Trichogrammatidae) and the zoo-phytophagous predator Nesidiocoris tenuis (Hemiptera, Miridae). The results indicate that although there is considerable quantitative and qualitative variation in the emission of VOCs in the 10 tomato cultivars analysed, this variability made it difficult to determine the influence of the volatiles on the attraction of the predatory species N. tenuis, with only one cultivar (Rebelion) exhibiting a significantly higher attractiveness than the rest of the cultivars. For the parasitoid T. achaeae, these same volatiles had a significant effect (in part) on parasitoid behaviour. However, this attraction was not reflected in the discriminant analysis, at least for the volatiles analysed. The analysis showed four groups of well-differentiated cultivars, according to the non-HIPV composition, and this bore no relation to the levels of attractiveness registered in the different cultivars, with the exception again of the Rebelion cultivar, which seems not to be very attractive for the parasitoid and its parasitism activity. The implications of non-herbivore-induced (non-HPV) VOCs in the biological control of greenhouse pest species are described and discussed. Full article

The livestock industry is undergoing significant transformation with the integration of intelligent technologies aimed at enhancing productivity, welfare, and sustainability. This review explores the latest advancements in intelligent systemization (IS), including real-time monitoring, machine learning (ML), and the Internet of Things (IoT), and their impacts on livestock farming. The aim of this study is to provide a comprehensive overview of how these technologies can address industry challenges by improving animal health, optimizing resource use, and promoting sustainable practices. The methods involve an extensive review of the current literature and case studies on intelligent monitoring, data analytics, automation in feeding and climate control, and renewable energy integration. The results indicate that IS enhances livestock well-being through real-time health monitoring and early disease detection, optimizes feeding efficiency, and reduces operational costs through automation. Furthermore, these technologies contribute to environmental sustainability by minimizing waste and reducing the ecological footprint of livestock farming. This study highlights the transformative potential of intelligent technologies in creating a more efficient, humane, and sustainable livestock industry. Full article

The article presents factors influencing the germination and development of plants after seeding with disk seeders. Schemes of improved two-disk seeders are proposed, forces acting on the improved seeder during operation, determination of the maximum distance between the seeder disks at the field surface level, and calculation schemes for determining the draft resistance of the serial and improved seeders, the area of the flat disk segment of the seeder, determination of the deformer, and tailstock area of the pressing plate. During the theoretical study of the seeding process, the following parameters and observations were obtained: analytical dependencies of soil density created by the pressing plate; geometric parameters of the pressing plate with a curvature radius r = 52…57 mm, plate section thickness of 2.5 mm; installation of the pressing plate insignificantly increases the draft resistance of the seeder; and the depth of the seeder’s travel has the greatest influence on spring deformation. Experimental studies reveal that the stiffness of the pressing plate is 7500…7600 N/m, ensuring an optimal furrow bottom density of 1.1–1.3 g/cm³; in the range of seed embedding depth of 0.05…0.07 m, 89% of the total number of seeds are placed compared to 76% of seeds embedded by the serial seeder. Full article

Over the last two decades, a considerable amount of equipment has been acquired (spreaders, seeders, sprayers, among others) to respond to the challenges of the precision agriculture (PA) concept. Most of this equipment has been purchased at a high cost. However, many of them, despite still being functional and equipped with sensors, actuators, and electronic processing units capable of adjusting to variations in speed, have become obsolete in terms of communication and incompatible with new monitoring and control systems based on the “Isobus” protocol. This work aims to present a solution for updating the control system (“Ferticontrol”) of a “Vicon RS-EDW” spreader with variable rate application (VRA), making it compatible with the “InCommand” system from “Ag Leader”. The solution includes serial protocol mediation using low-cost tools such as “Arduino” and “Raspberry Pi” microcontrollers and open-source software. The development shows that it is possible to implement a solution that is accessible to farmers in general. It also provides a niche business opportunity for young researchers to set up small technology-based enterprises associated with universities and research centers. These partnerships guarantee permanent innovation and represent a decisive step towards modern, technological, competitive, and sustainable agriculture. Full article

This paper presents a machine learning (ML)-based approach for the intelligent control of Autonomous Vehicles (AVs) utilized in solar panel cleaning systems, aiming to mitigate challenges arising from uncertainties, disturbances, and dynamic environments. Solar panels, predominantly situated in dedicated lands for solar energy production (e.g., agricultural solar farms), are susceptible to dust and debris accumulation, leading to diminished energy absorption. Instead of labor-intensive manual cleaning, robotic cleaners offer a viable solution. AVs equipped to transport and precisely position these cleaning robots are indispensable for the efficient navigation among solar panel arrays. However, environmental obstacles (e.g., rough terrain), variations in solar panel installation (e.g., height disparities, different angles), and uncertainties (e.g., AV and environmental modeling) may degrade the performance of traditional controllers. In this study, a biologically inspired method based on Brain Emotional Learning (BEL) is developed to tackle the aforementioned challenges. The developed controller is implemented numerically using MATLAB-SIMULINK. The paper concludes with a comparative analysis of the AVs’ performance using both PID and developed controllers across various scenarios, highlighting the efficacy and advantages of the intelligent control approach for AVs deployed in solar panel cleaning systems within agricultural solar farms. Simulation results demonstrate the superior performance of the ML-based controller, showcasing significant improvements over the PID controller. Full article