AgriEngineering, Volume 4, Issue 4 (December 2022) – 24 articles

This paper addresses the problem of adapting a control system to unseen conditions, specifically to the problem of trajectory tracking in off-road conditions. Three different approaches are considered and compared for this comparative study: The first approach is a classical reinforcement learning method to define the steering control of the system. The second strategy uses an end-to-end reinforcement learning method, allowing for the training of a policy for the steering of the robot. The third strategy uses a hybrid gain tuning method, allowing for the adaptation of the settling distance with respect to the robot’s capabilities according to the perception, in order to optimize the robot’s behavior with respect to an objective function. The three methods are described and compared to the results obtained using constant parameters in order to identify their respective strengths and weaknesses. They have been implemented and tested in real conditions on an off-road mobile robot with variable terrain and trajectories. The hybrid method allowing for an overall reduction of $53.2\%$ when compared with a predictive control law. A thorough analysis of the methods are then performed, and further insights are obtained in the context of gain tuning for steering controllers in dynamic environments. The performance and transferability of these methods are demonstrated, as well as their robustness to changes in the terrain properties. As a result, tracking errors are reduced while preserving the stability and the explainability of the control architecture. Full article

Infectious animal diseases can cause severe mortality on infected farms. An outbreak challenges the system and forces difficult decisions to stop the disease progression. We propose an ‘all-in-one’ concept of a mobile system for on-farm swine depopulation and pathogen inactivation. The system uses vaporized CO₂ followed by heat treatment, broadening options for off-site carcass disposal and cleanup. A direct-fired heater supplies heat into the insulated trailer to reach and maintain the inactivation temperature for targeted pathogens. We developed a user-friendly model based on engineering principles for estimating site- and scenario-specific CO₂ amounts and times required to inactivate targeted pathogens. Multipoint CO₂ injection and improved distribution to animals follow the plug-flow reactor air replacement model. The model illustrates the depopulation and inactivation of two diseases, African swine fever (ASF) and the porcine reproductive and respiratory syndrome (PRRS) viruses. The model allows for dump trailer size, pig number, weights, and environmental conditions input. Model outputs provide users with practical information about the required CO₂ injection rate, temperature setpoints, and times to effectively depopulate and inactivate pathogens in carcasses. The concept could be adopted for a routine or a mass depopulation/treatment/disposal with a single or fleet of ‘all-in-one’ units. Full article

Brazil is one of the world’s biggest monogastric producers and exporters (of pig and broiler meat). Farmers need to improve their production planning through the reliability of animal growth forecasts. Predicting pig and broiler growth is optimizing production planning, minimizing the use of resources, and forecasting meat production. The present study aims to apply a hybrid metaheuristic algorithm (SAGAC) to find the best combination of values for the growth curve model parameters for monogastric farm animals (pigs and broilers). We propose a hybrid method to optimize the growth curve model parameters by combining two metaheuristic algorithms Simulated Annealing (SA) and Genetic Algorithm (GA), with the inclusion of a function to promote the acceleration of the convergence (GA + AC) of the results. The idea was to improve the coefficient of determination of these models to achieve better production planning and minimized costs. Two datasets with age (day) and average weight (kg) were obtained. We tested three growth curves: Gompertz, Logistic, and von Bertalanffy. After 300 performed assays, experimental data were tabulated and organized, and a descriptive analysis was completed. Results showed that the SAGAC algorithm provided better results than previous estimations, thus improving the predictive data on pig and broiler production consistency. Using SAGAC to optimize the growth parameter models for pigs and broilers led to optimizing the results with the nondeterministic polynomial time (NP-hardness) of the studied functions. All tuning of the growth curves using the proposed SAGAC method for broilers presented R² above 99%, and the SAGAC for pigs showed R² above 94% for the growth curve. Full article

An exclusively mechanical stand-alone automatic device, self-adjusting to weather changes for controlled irrigation, that operates only on the energy of piped water, without electricity, is the described low-cost “Hydraulic Evapotranspiration Multisensor-HEM”. It is composed of an Evaporation Pan with water left to evaporate, a Floater with a Magnet floating in this water, a Hydraulic Device managing a Hydraulic Water Valve having means to adjust irrigation frequency, and a system that returns water to said Pan, through an Adjustable Dripper, to replace that lost by evaporation. During the Evaporation Phase, gradually the water level is lowered to a predetermined level, at which the floating Magnet acts on said Hydraulic Device to start irrigation. Water from the irrigation line is returned to the Evaporation Pan at the proper for the irrigation time rate. When the lost water is replaced irrigation is terminated and the system resets. On installation Irrigation Frequency and Irrigation Time are set with two graduated screws, for normal weather and the conditions of the particular plantation. HEM responding to weather changes modifies the irrigation schedule set, either by shortening, at a high evaporation rate, the time interval between consecutive irrigation cycles to protect plantations from water deficit stress or extending this time interval at a low evaporation rate to save water. Assessing the performance of HEM, by taking the estimations of evapotranspiration from the Penman–Monteith method shows high accuracy in the studied site. Considering the advantages of the product against the programmable irrigation controller devices, HEM provides optimum irrigation control in field crops and makes it a powerful “green tool” to be used in Mediterranean greenhouses. Full article

High-pressure processing (HPP) is a novel technology that is used in many food processing operations to increase both the efficiency and a reduction in the energy and time required to modify and improve the physical and chemical properties of traditional food products. The objective of this study was to investigate the impacts of applying three treatments of a high hydrostatic pressure (HHP) and a control, i.e., 0, 100, 300 and 600 MPa, on the water absorption, gelatinization properties, and microstructural changes of wheat grains. The results indicated that the HHP treatments with a pressure of 300 and 600 MPa resulted in an increase of 16.7–24.8% in the mass of the grains; however, the pressure of 600 MPa did not result in a mass increase through water uptake. Further, the transition enthalpy increased with the HHP pressure, with 600 MPa defined as the threshold value for pressure. The results from this study demonstrated that a HHP treatment may enhance the soaking process of wheat grains and, thus, positively affect their gelatinization properties. These preliminary results may be used to improve the processing efficiency and quality of wheat-based products. Full article

Sowing is critical for successful crop establishment and productivity, particularly in precision agriculture management strategies. However, topsoil characteristics directly affect agribusiness maximization (i.e., crop-yield increase, machinery efficiency, operating-cost reduction) even in the most advanced farming management techniques. The excessive presence of coarse fractions or stones in arable soil layers prevents modern machinery from reaching optimal efficiency. This work focuses on sowing to verify whether the vibration and noise arising during this operation significantly change with varying soil conditions according to the stoniness degree of disturbance on soil workability. To make this assessment, an experimental sowing activity was carried out on four soil plots with two different disturbance degrees. The results confirmed that the noise and acceleration of the sowing machine significantly correlated with the soil disturbance degree and related workability profile. Full article

Honey is a natural product made by honeybees, its composition depends on factors such as climate, soil and plant source. In this study, the nutritional parameters, phenolic composition, antioxidant activity and antibacterial ability of 30 different types of honey of different botanical and geographical origins in Vietnam were investigated. The study focused on the characterization and evaluation of the influence of plant origin and geographical location on physical–chemical properties and biological activities (antioxidant and antibacterial). The obtained results show that all honey samples meet quality standards according to international standards and Vietnamese standards, except for some exceptions recorded in moisture, 5-hydroxymethylfurfural (HMF) value and ash. These samples were explored for the detection of 13 polyphenols by using high-performance liquid chromatography (HPLC). The classification of honey samples collected from different regions and botanical sources was performed by principal component analysis (PCA), and it was observed that certain phenolic compounds contributed to the identification of honey samples. In addition, the correlation between physicochemical properties, chemical composition and biological activity of most honeys was also first clarified in this study. Overall, our data provide an overview data set and essential results in creating a database on the world honey trait map. Full article

Response to the expanding demand for high-quality citrus saplings plants requires optimisation and a deep understanding of production climate behaviour. In this context, greenhouse production is the most used technique because it allows farmers to effectively monitor plant growth through production condition control, especially climatic parameters. The current work presents an analysis of climate behaviour and plant heat activity of a citrus sapling tunnel greenhouse in the middle region of Morocco. In this regard, a computational fluid dynamic (CFD) model was developed and validated with respect to temperature and relative humidity measured values. The specificity of this model is the inclusion of a new non-grey radiative and heat transfers physical sub-models to couple the convective and radiative exchanges at the plastic roof cover and crop level. The findings showed that using a green shade net increased the greenhouse shadow, and the layering of plastic and shade net significantly reduced solar radiation inside the greenhouse by 50%. Also, the greenhouse airflow speed was deficient; it cannot exceed $0.3$ms⁻¹, hence the dominance of the chimney effect in heat transfer. Despite the previous results, analyses of greenhouse temperature and relative humidity fields clearly showed the greenhouse climate behaviour heterogeneity, where spatial greenhouse air temperature and relative humidity difference values reached a maximum of 29.7 °C and 23%, respectively. For citrus plants, heat activity results showed that a weak fraction (1.44%) of the short wavelength radiation is converted to latent heat, which explains the low plant transpiration under these conditions. While the convective currents are the primary source of temperature and relative humidity heterogeneity inside the greenhouse, the presence of crop rows tends to homogenise the climate inside the greenhouse. We also concluded the necessity of proper condensation modelling near ground surfaces and inside the crop, and the water vapour effect on climate determination. Full article

In the present study, wheatgrass (Triticum aestivum L.) was chosen since fish effluents could be adequate to support its optimal growth. Wheatgrass was irrigated by water from African catfish (Clarias gariepinus) production under two stocking densities, namely extensive aquaculture unit (EAU) and intensive aquaculture unit (IAU), and tap water mixed with fertilizer (Control) in combination with three horticultural substrates: 100% coconut fibers (C), 70% coconut fibers + 30% perlite (CP), and 50% perlite + 50% vermiculite (PV) in an ebb-and-flow system. Different plant growth parameters, including nutrient contents, were evaluated and discussed. The results showed that regarding irrigation water, shoot dry mass was significantly higher in fish water groups. The root–shoot ratio was significantly higher in Control. The highest SPAD index was discovered in IAU. Regarding substrates, the root–shoot ratio was significantly low in C. Vitamin and mineral production reached mainly the highest concentrations in the combination of fish water with different substrates. In conclusion, irrigation water from IAU and coconut fibers were sufficient for optimal wheatgrass growth; meanwhile, IAU effluents showed a positive influence on vitamin production. Our study demonstrates the potential of aquaponics as a more sustainable way of producing superfoods. Full article

In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the efforts to establish a framework towards quantifying the crop yield prediction at regional scales are limited, we investigated the utility of combining crop model with the regional weather prediction model to forecast winter wheat yields over space. The exercise was performed for various lead-times in the regions of Punjab and Haryana for the years 2008–2009. A numerical weather prediction (NWP) model was used to generate micro-meteorological variables at different lead times (1-week, 2-weeks, 3-weeks and 5-weeks) ahead of crop harvest and used within the CERES-Wheat crop simulation model gridded framework at a spatial resolution of 10 km. Various scenarios of the yield forecasts were verified with district-wide reported yield values. Average deviations of −12 to 3% from the actual district-wise wheat yields were observed across the lead times. The 3-weeks-ahead yield forecasts yielded a maximum agreement index of 0.86 with a root mean squared error (RMSE) of 327.75 kg/ha and a relative deviation of −5.35%. The critical crop growth stages were found to be highly sensitive to the errors in the weather forecast, and thus made a huge impact on the predicted crop yields. The 5-weeks-ahead weather forecasts generated anomalous meteorological data during flowering and grain-filling crop growth stages, and thus had the highest negative impact on the simulated yields. The agreement index of the 5-week-ahead forecasts was 0.41 with an RMSE of 415.15 kg ha⁻¹ and relative deviation of −2.77 ± 5.01. The proposed methodology showed significant forecast skill for extended space and time scale crop yield forecasting, offering scope for further research and practical applicability. Full article

Climate change is often linked with record-breaking heavy or poor rainfall events, unprecedented storms, extreme day and night time temperatures, etc. It may have a marked impact on climate-sensitive sectors and associated livelihoods. Block-level weather forecasting is a new-fangled dimension of agrometeorological services (AAS) in the country and is getting popularized as a climate-smart farming strategy. Studies on the economic impact of these microlevel advisories are uncommon. Agromet advisory services (AAS) play a critical role as an early warning service and preparedness among the maize farmers in the Parambikulam–Aliyar Basin, as this area still needs to widen and deepen its AWS network to reach the village level. In this article, the responses of the maize farmers of Parambikulam–Aliyar Basin on AAS were analyzed. AAS were provided to early and late Rabi farmers during the year 2020–2022. An automatic weather station was installed at the farmers’ field to understand the real-time weather. Forecast data from the India Meteorological Department (IMD) were used to provide agromet advisory services. Therefore, the present study deserves special focus. Social media and other ICT tools were used for AAS dissemination purposes. A crop simulation model (CSM), DSSAT4.7cereal maize, was used for assessing maize yield in the present scenario and under the elevated GHGs scenario under climate change. Our findings suggest that the AAS significantly supported the farmers in sustaining production. The AAS were helpful for the farmers during the dry spells in the late samba (2021–2022) to provide critical irrigation and during heavy rainfall events at the events of harvest during early and late Rabi (2021–22). Published research articles on the verification of weather forecasts from South India are scanty. This article also tries to understand the reliability of forecasts. Findings from the verification suggest that rainfall represented a fairly good forecast for the season, though erratic, with an accuracy score or HI score of 0.77 and an HK score of 0.60, and the probability of detection (PoD) of hits was found to be 0.91. Verification shows that the forecasted relative humidity observed showed a fairly good correlation, with an R² value of 0.52. These findings suggest that enhancing model forecast accuracy can enhance the reliability and utility of AAS as a climate-smart adaptation option. This study recommends that AAS can act as a valuable input to alleviate the impacts of hydrometeorological disasters on maize crop production in the basin. There is a huge demand for quality weather forecasts with respect to accuracy, resolution, and lead time, which is increasing across the country. Externally funded research studies such as ours are an added advantage to bridge the gap in AAS dissemination to a great extent. Full article

Size-reduction of small grain residue is required on the combine harvester to promote uniform distribution of residue across the full harvested width. However, unnecessary size reduction increases energy expenditures that can reduce harvester capacity. To objectively quantify the degree of residue processing, an apparatus and method was developed for evaluating particle-size distribution of small grain crop residue. The apparatus consisted of a pre-screener to sort long particles and an oscillating cascade of three screens which separated material into four additional fractions. The separation process was continuous, so large volume samples could be separated more quickly than batch systems. The developed system was used to evaluate wheat residue which was processed to various extents by a combine residue chopper in two experiments. Statistically significant (p < 0.05) differences between variably processed wheat residues were found using the developed apparatus and methodology. The separated wheat residue was partitioned into three particle-size ranges of less than 50 mm, 50 to 125 mm, and greater than 125 mm. Samples of 3 to 4 kg could be completely analyzed in less than 10 min. Full article

Agriculture is the foremost source of food for humans. Fossil fuels are typically used to operate farm machines, contributing to carbon emissions and accelerating climate change. It is possible to mitigate environmental damage by promoting renewable or alternative fuels, namely biofuels, solar energy, biomass, wind, geothermal, small-scale hydro, and wave power. Biofuels are considered as low carbon-emitting alternatives to conventional fuels. The use of biofuels promotes reduced emissions of greenhouse gases and reduces the related detrimental impact of transport. As an alternative to fossil fuels, renewable fuels seem to present a promising scenario. However, if low carbon products are promoted, analysis of each particular product’s GHG emissions and carbon footprint (CF) is needed. Nowadays, CF is considered as the prime indicator of environmental impact, and its calculation is in utmost demand. Agriculture significantly benefits from the use of renewable resources. The carbon footprint measurement has the potential to assess and compare carbon emissions generated by agricultural products and to identify points for improving environmental performance. Several studies have compared alternative fuels with conventional fuels, and it has been proven that using alternative fuels can significantly reduce traditional fuel consumption. Bioenergy includes a number of socio- economic, technical as well as environmental benefits that helps in achieving the UN sustainable development goals (SDG). The aim to end malnutrition and hunger (SDG 2) requires a sustainable system for food production as well as resilient agriculture practices to improve agricultural productivity. The revenues from bioenergy projects can provide food and a better diet for small farming communities, thereby improving their quality of life. The present review aims to provide a comprehensive outlook of the role of alternative or biofuels in the agriculture sector, in terms of economic feasibility and carbon footprint, for sustainable development. This review also discusses the various generations of biofuels in attaining carbon neutrality, biofuel’s impact on the environment, applications in agriculture, and limitations. Full article

Agricultural greenhouses have been identified as a niche application for organic photovoltaic (OPV) integration, leveraging key performance characteristics of OPV technology, including semi-transparency, light weight, and mechanical flexibility. For optimal electrical design and performance assessment of greenhouse-integrated OPV systems, knowledge of the solar irradiance incident on OPV module surfaces is essential. Many greenhouse designs feature roof curvature. For flexible OPV modules deployed on curved greenhouse roofs, this results in a non-homogenous distribution of solar radiation across the module surfaces, which affects electrical output. Conventional modeling methods for estimating solar irradiance on a PV surface assume planarity, and therefore they are insufficient to evaluate OPV (and other flexible PV) installations on curved greenhouse structures. In this study, practical methods to estimate incident solar irradiance on curved surfaces were developed and then applied in an outdoor performance evaluation of large-area, roll-to-roll printed OPV arrays (3.4 m² active area) installed on a gothic-arch greenhouse roof in Tucson, Arizona between October–February. The outdoor performance of six OPV arrays was assessed using the curved-surface modeling tools primarily considering the effect of irradiance on electrical behavior. The OPV arrays had an overall power conversion efficiency (PCE) of 1.82%, with lower PCE in the afternoon periods compared to morning and midday periods. The OPV arrays experienced an average 32.6% loss in normalized PCE over the course of the measurement period. Based on these results, we conclude that the higher performing OPV devices that are more robust in outdoor conditions coupled with accurate performance monitoring strategies are needed to prove the case for agrivoltaic OPV greenhouses. Full article

Potato waste is generated in a high amount, stably over the year, by operators capable of recovering it. Currently, it is valorized as feed, bioethanol, or biogas. This work explores another avenue to increase the valorization of this waste: the production of yeast production to serve as fodder or single-cell protein. First, potatoes were deconstructed into fermentable sugars by acid hydrolysis using food-grade techniques. Then, after pH adjustment, Saccharomyces cerevisiae was inoculated, and cell growth was monitored. For optimization purposes, this procedure was led over a large range of temperature (90–120 °C) and operation time (30–120 min), for a 1/2 solid/liquid ratio. Response surfaces methodology allowed to achieve a maximum sugar release (44.4 g/L) for 99 min under 103 °C. Then, a numerical model combining biological performances and factory process planning was used to derive process productivity (the best compromise between sugar release and cell growth). Maximal productivity (82.8 g_Yeast/w/L in batch mode, 110 g_Yeast/w/L in fed-batch mode) was achieved for 103 min under 94 °C. Furthermore, the process’s robustness was confirmed by a sensibility analysis. Finally, as the proposed procedure preserves the food-grade quality of the substrate, the produced yeast can be used as food or feed. Full article

Agricultural machinery telemetry collects and shares data, which are sent remotely and become precious information. Thus, accurate and instantaneous monitoring can provide an important base of information for adjusting the parameters of the most diverse mechanized agricultural operations, reducing input costs and maintenance expenses. In recent years, this theme has gained more strength and importance for managing rural properties. Therefore, the present study developed a bipartite bibliometric analysis in two lines of research and described the state of the art of this data collection methodology (via telemetry), presenting its technological evolution. The study presents the evolution and connection of telemetry and the processes of robotization of agricultural operations and automation provided by data collection via telemetry in real time. The main countries, keywords, researchers, institutions, and the Dickson quality index indicate a high growth in the last decade. Thus, the present study contributes to decision making regarding research topics, guidance on the state of the art, and contextualization of telemetry’s importance in current research. Full article

Olive oil is an important component of Mediterranean diet widely, consumed thanks to its numerous health-healing properties. Its quality is dependent upon a set of factors (genotypic, environmental, agronomic practices, ripening, etc). These are well documented, but little is known about the impact of extraction technology on ‘Moroccan Picholine’ olive oil quality. In this paper, physicochemical traits of olive oil (cv ‘Moroccan Picholine’) were investigated according to extraction technology namely super pressure (SP), 2-phase (2P), and 3-phase (3P) systems as well as traditionally extracted oil (Alwana Oil, AO). The obtained results revealed significant differences (p < 0.05) in terms of the studied physicochemical traits. The investigated oil samples were classified as extra-virgin olive oil. Oil samples from super pressure and AO marked by high records of peroxide value, acidity, K270, fatty acids and trans fatty acids likely due to partial oxidation during extraction. AO was marked by high MUFA, stigmasterol, brassicosterol, 2P displayed high SFA and β-sitosterol, and 3P had high PUFA, SFA, ∆7-avenasterol, and ∆7-stigmasterol. These results were confirmed by principal component analysis, cluster analysis and artificial neural networks. In conclusion, continuous systems (2- and 3-phase) produced olive oil of better quality as compared to super-pressure and traditionally extracted oil. Full article

Because our civilization has relied on pesticides to fight weeds, insects, and diseases since antiquity, the use of these chemicals has become natural and exclusive. Unfortunately, the use of pesticides has progressively had alarming effects on water quality, biodiversity, and human health. This paper proposes to improve farming practices by replacing pesticides with a laser-based robotic approach. This study focused on the neutralization of aphids, as they are among the most harmful pests for crops and complex to control. With the help of deep learning, we developed a mobile robot that spans crop rows, locates aphids, and neutralizes them with laser beams. We have built a prototype with the sole purpose of validating the localization-neutralization loop on a single seedling row. The experiments performed in our laboratory demonstrate the feasibility of detecting different lines of aphids (50% detected at 3 cm/s) and of neutralizing them (90% mortality) without impacting the growth of their host plants. The results are encouraging since aphids are one of the most challenging crop pests to eradicate. However, enhancements in detection and mainly in targeting are necessary to be useful in a real farming context. Moreover, robustness regarding field conditions should be evaluated. Full article

Coffee has high relevance in the Brazilian agricultural scenario, as Brazil is the largest producer and exporter of coffee in the world. Strategies to advance the production of coffee grains involve better understanding its spatial variability along fields. The objectives of this study were to adjust yield-prediction models based on a time series of satellite images and high-density yield data, and to indicate the best phenological stage of coffee crop to obtain satellite images for this purpose. The study was conducted during three seasons (2019, 2020 and 2021) in a commercial area (10.24 ha), located in the state of Minas Gerais, Brazil. Data were obtained using a harvester equipped with a yield monitor that measures the volume of coffee harvested with 3.0 m of spatial resolution. Satellite images from the PlanetScope (PS) platform were used. Random forest (RF) regression and multiple linear regression (MLR) models were fitted to different datasets composed of coffee yield and time series of satellite-image data ((1) Spectral bands—red, green, blue and near-infrared; (2) Normalized difference vegetation index (NDVI); or (3) Green normalized difference vegetation index (GNDVI)). Whether using RF or MLR, the spectral bands, NDVI and GNDVI reproduced the spatial variability of yield maps one year before harvest. This information can be of critical importance for management decisions across the season. For yield quantification, the RF model using spectral bands showed the best results, reaching R² of 0.93 for the validation set, and the lowest errors of prediction. The most appropriate phenological stage for satellite-image data acquisition was the dormancy phase, observed during the dry season months of July and August. These findings can help to monitor the spatial and temporal variability of the fields and guide management practices based on the premises of precision agriculture. Full article

Tomato leaves can have different diseases which can affect harvest performance. Therefore, accurate classification for the early detection of disease for treatment is very important. This article proposes one classification model, in which 16,010 tomato leaf images obtained from the Plant Village database are segmented before being used to train a deep convolutional neural network (DCNN). This means that this classification model will reduce training time compared with that of the model without segmenting the images. In particular, we applied a VGG-19 model with transfer learning for re-training in later layers. In addition, the parameters such as epoch and learning rate were chosen to be suitable for increasing classification performance. One highlight point is that the leaf images were segmented for extracting the original regions and removing the backgrounds to be black using a hue, saturation, and value (HSV) color space. The segmentation of the leaf images is to synchronize the black background of all leaf images. It is obvious that this segmentation saves time for training the DCNN and also increases the classification performance. This approach improves the model accuracy to 99.72% and decreases the training time of the 16,010 tomato leaf images. The results illustrate that the model is effective and can be developed for more complex image datasets. Full article

A no-till experiment was conducted in Auburn, AL U.S.A. to evaluate the effectiveness of an experimental two-stage roller/crimper in reoccurring rolling over the same area planted with a cereal rye/crimson clover cover crop mix and its influence on bush bean yield. Cover crop termination was much greater with rolling/crimping when compared to the non-rolled (untreated) control. During the three growing seasons, rolling three times had significantly higher termination rates compared to all other treatments, exceeding 90% in 2020. These results suggest that there may be an advantage to rolling/crimping three times so that planting of the cash crop could potentially be performed one week earlier, under favorable soil moisture conditions. However, for growing seasons 2018 and 2020 at three weeks after rolling, there were no differences between rolling treatments. In 2019, rolling three times over the same cover crop area was the only treatment that achieved above 90% termination rate indicating a clear advantage of recurring rolling/crimping in 2019. Rolling/crimping proved to be effective as yield was significantly higher compared to not rolled when averaged over all three growing seasons. This is possible due to the difficulty in planting into a standing cover crop which could have negative effects on seed to soil contact, but more importantly explained with the slight soil moisture advantage given to the rolled plots over the standing cover crop plots. Thus, optimum soil moisture when planting beans is key for successful germination and good main crop stand. Full article

Wood-related procedures, such as lumberjacking and pruning, inevitably result in big piles of leaves, which are considered a major by-product. Extracting valuable compounds from natural by-products is an ongoing trend. In this work, the use of Pulsed Electric Field (PEF) was evaluated as a pretreatment step, prior to the ultrasound-assisted extraction of phenolic compounds from Aesculus carnea leaves. In addition, various solvent systems were examined, as well as the time of pretreatment with PEF. According to the results, up to 33% more phenolic compounds can be extracted, under optimum conditions (30% ethanol in water as solvent and PEF pretreatment for 30 min, compared to the same solvent, without PEF). Moreover, PEF treatment time was not (i.e., 30 and 60 min) and no differences were recorded, suggesting that a lower treatment time can yield the same extraction of phenolic compounds. As such, the use of PEF is highly recommended in combination with ultrasound extraction, to maximize the yield of phenolic compounds extracted from the leaves of Aesculus carnea. Full article

The intensive advances in robotics have deeply facilitated the accomplishment of tedious and repetitive tasks in our daily lives. If robots are now well established in the manufacturing industry, thanks to the knowledge of the environment, this is still not fully the case for outdoor applications such as in agriculture, as many parameters are varying (kind of vegetation, perception conditions, wheel–soil interaction, etc.) The use of robots in such a context is nevertheless important since the reduction of environmental impacts requires the use of alternative practices (such as agroecological production or organic production), which require highly accurate work and frequent operations. As a result, the design of robots for agroecology implies notably the availability of highly accurate autonomous navigation processes related to crop and adapting to their variability. This paper proposes several contributions to the problem of crop row tracking using a four-wheel-steering mobile robot, which straddles the crops. It uses a 2D LiDAR allowing the detection of crop rows in 3D thanks to the robot motion. This permits the definition of a reference trajectory that is followed using two different control approaches. The main targeted application is navigation in vineyard fields, to achieve several kinds of operation, such as monitoring, cropping, or accurate spraying. In the first part, a row detection strategy based on a 2D LiDAR inclined in front of the robot to match a predefined shape of the vineyard row in the robot framework is described. The successive detected regions of interest are aggregated along the local robot motion, through the system odometry. This permits the computation of a local trajectory to be followed by a robot. In a second part, a control architecture that allows the control of a four-wheel-steering mobile robot is proposed. Two different strategies are investigated, one is based on a backstepping approach, while the second considers independently the regulation of front and rear steering axle position. The results of these control laws are then compared in an extended simulation framework, using a 3D reconstruction of actual vineyards in different seasons. Full article

This paper used the 4UM-120D electric leafy vegetable harvester as the research object and designed a travel speed automatic control system to maintain the travel speed within a set value of ±2% in order to improve the efficiency and quality of leafy vegetable harvester operations and decrease the work intensity of the operator. The harvester’s travel speed was automatically controlled by using the PID, adaptive fuzzy PID, and sliding mode control techniques after the mechanical and electrical equations for the travel drive motor (a DC brushless motor) were established in MATLAB. By simulating various working situations, the stability, accuracy, and speed of the automatic control system were compared and analyzed using the adjustment time, overshoot, steady-state transition time, and maximum deviation from the set speed as evaluation indicators. The test results revealed that when the current value of the leafy vegetable harvester travel speed deviated from the set value by more than 2%, the dynamic response performance and stability of the DC brushless motor travel drive system based on the sliding mode control strategy was significantly better than that of the PID and adaptive fuzzy PID control strategies, and its anti-disturbance was stronger, achieving the function of automatic control of the harvester travel speed. When the travel motor started with a constant load and the sliding mode control strategy’s parameters were the gain factors A = 1/70, c = 100, ε = 100, and k = 100, the travel drive system regulation time was 1.5 s, and the overshoot was 10%. When the harvester was operating smoothly and had leafy vegetable collection baskets loaded and unloaded, the steady-state transition time was 0.3 s. According to the actual engineering application experience, the specific technical state of the control strategy of the agricultural machinery travel speed automatic control system was: regulation time 2.5~3 s; overshoot amount 20~25%; and steady-state transition time 1.0~1.5 s, so the travel speed automatic control system of the electric leafy vegetable harvester in sliding mode was in line with the technical state requirements. The results of the field trials demonstrated the accuracy of the simulation test results. This study offered a method to lessen the work intensity of operators and increase the operating efficiency and quality of a leafy vegetable harvester. Full article