AgriEngineering

Caatinga is a typical Brazilian biome facing severe threats despite its ecological and socio-economic importance. Conservation strategies are essential in protecting ecosystems and ensuring natural resource sustainability. Mata da Pimenteira State Park (PEMP), launched in 2012, is an example of such a strategy. The current study aims to use orbital remote sensing techniques to assess human impacts on changes in land use and land cover (LULC) after the establishment of PEMP in the semi-arid region known as Caatinga, in Pernambuco State. The effects of this unit on vegetation preservation were specifically analyzed based on using data from the MapBiomas Brasil project to assess trends in LULC, both in and around PEMP, from 2002 to 2020. Man–Kendall and Pettitt statistical tests were applied to identify significant changes, such as converting forest areas into pastures and agricultural plantations. Trends of the loss and gain of LULC were observed over the years, such as forest areas’ conversion into pasture and vice versa, mainly before and after PEMP implementation. These findings highlight the importance of developing conservation measures and planning to help protecting the Caatinga, which is a vital biome in Brazil. Full article

With recent advances in spray technology and rising interest in site-specific applications, it is imperative to assess the performance of the latest application technologies to ensure effective pesticide applications. Thus, a study was conducted to compare and evaluate the performance of two different flow control systems [rate controller (RC) and pulse width modulation (PWM)] on an agricultural sprayer while simulating different site-specific application scenarios. A custom data acquisition and logging system was developed to record the real-time nozzle flow and pressure across the sprayer boom. The first experiment measured the response time to achieve different target application rates in single-rate site-specific (On/Off) states at varying simulated ground speeds. The second experiment examined the response time for rate transitions in variable-rate application scenarios among different selected target rates at varying simulated ground speeds. Across all the application scenarios, the PWM system consistently outperformed the RC system in terms of response time and rate stabilization. Specifically, the PWM system exhibited significantly lower mean rate stabilization times compared to the RC system during single-rate application states. Similarly, in the variable-rate application states—where the rate transitions were evaluated—the PWM system consistently displayed shorter mean rate transition and stabilization times compared to the RC system. Overall, the findings from this study suggest PWM systems tend to be more responsive and effective, making them the preferred choice for efficient precision site-specific pesticide applications. Future research should evaluate the influence of other operational parameters such as look-ahead time and ground speed variations on the performance of both systems in actual field applications. Full article

The development of methods to detect plant stress is not only a scientific challenge, but is also of great importance for agriculture and forestry. However, at present, stress diagnostics based on plant spectral characteristics has several limitations: (1) the high dependence of stress assessment on plant species identity; (2) the poor differentiation of different types of stress; and (3) the difficulty of detecting stress before visible symptoms appear. In this regard, the development of plant spectral metrics represents a significant area of research. Ficus elastica plants were exposed under the photosynthetic photon flux density (PPFD) from 0 to 1200 μmol photons m⁻²s⁻¹. Exposure of F. elastica leaves to excess light (EL) (≥400 μmol photons m⁻²s⁻¹) resulted in an increase in reflectance in the yellow-green region (522–594 nm) and a decrease in reflectance in the red region (666–682 nm) of the spectrum, accompanied by a shift of the red edge point toward the longer wavelength. These changes were revealed using the previously proposed light stress index (LSI = mean(R_666:682)/mean(R_522:594)). Based on the results obtained, two new vegetation indices (VIs) were proposed: LSI_Red = R₆₇₄/R₆₅₄ and LSI_Norm = (R₆₇₄ − R₆₅₄)/(R₆₇₄ + R₆₅₄), indicating light stress by changes in the red region of the spectrum. The results of the study showed that LSI, LSI_Red, and LSI_Norm have a high degree of coupling strength with maximal quantum yields of photosystem II values. The plant response to EL exposure, as assessed by the values of these three VIs, was well expressed regardless of the PPFD levels. The effect of EL at non-stressful PPFDs (50–200 μmol photons m⁻²s⁻¹) was found to disappear within one hour after cessation of exposure. In contrast, the effect of the stressful PPFD (800 μmol photons m⁻²s⁻¹) was found to persist for at least 80 h after cessation of exposure. The results of the study indicate the need to consider light history in spectral monitoring of vegetation. Full article

Weeds are one of the primary concerns in agriculture since they compete with crops for nutrients and water, and they also attract insects and pests and are, therefore, hindering crop yield. Moreover, seasonal labour shortages necessitate the automation of such agricultural tasks using machines. For this reason, advances in agricultural robotics have led to many attempts to produce autonomous machines that aim to address the task of weeding both effectively and efficiently. Some of these machines are implementing chemical-based weeding methods using herbicides. The challenge for these machines is the targeted delivery of the herbicide so that the environmental impact of the chemical is minimised. However, environmental concerns drive weeding robots away from herbicide use and increasingly utilise mechanical weeding tools or even laser-based devices. In this case, the challenge is the development and application of effective tools. This paper reviews the progress made in the field of weeding robots during the last decade. Trends during this period are identified, and the current state-of-the-art works are highlighted. Finally, the paper examines the areas where the current technological solutions are still lacking, and recommendations on future directions are made. Full article

Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (>80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model. Full article

Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson’s correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction. Full article

Diversified cropping systems offer an alternative to sustainable agriculture, but they present high spatial variability. This study aims to develop and evaluate an automated irrigation system and a variable-rate water application for areas with diversified vegetable crops. The prototype comprises a mobile drip line, a winding reel, and an electronic control system. The drip line irrigates plants individually, with irrigation depths along the beds controlled by the displacement speed and between beds by adjusting the timing of electrical pulses to activate the water flow control valves. To evaluate the drip line, irrigation depths were defined for different crops, followed by performance assessments, which included evaluating the uniformity (Christiansen’s Uniformity Coefficient—CUC) of the line under constant and variable rates. A hydraulic evaluation of the system was also carried out, as well as the calculation of the potential irrigable area. The drip line showed CUC ≥96% for depths under a constant rate and 95% for depths under a variable rate. The application efficiency reached 93.4% for a degree of suitability of 83%, considering variable depths along and between beds. The potential irrigable area obtained was 360 m² day⁻¹. The developed drip line effectively meets the spatial variability of crop water requirements in diversified cropping systems by adopting the variable-rate irrigation technique. The control of irrigation depth through valve activation via electrical pulses allows for the application of variable depths between the beds. Full article

Heat stress stands out as one of the main elements linked to concerns related to animal thermal comfort. This research aims to develop a sequential methodology for the extraction of automatic characteristics from thermal images and the classification of heat stress in pigs by means of machine learning. Infrared images were obtained from 18 pigs housed in air-conditioned and non-air-conditioned pens. The image analysis consisted of its pre-processing, followed by color segmentation to isolate the region of interest and later the extraction of the animal’s surface temperatures, from a developed algorithm and later the recognition of the comfort pattern through machine learning. The results indicated that the automated color segmentation method was able to identify the region of interest with an average accuracy of 88% and the temperature extraction differed from the Therma Cam program by 0.82 °C. Using a Vector Support Machine (SVM), the research achieved an accuracy rate of 80% in the automatic classification of pigs in comfort and thermal discomfort, with an accuracy of 91%, indicating that the proposal has the potential to monitor and evaluate the thermal comfort of pigs effectively. Full article

Blueberries offer multiple health benefits, and their cultivation has expanded to warm tropical regions. However, references for foliar nutritional content are lacking in the literature. Proximal sensing may enhance nutritional characterization to optimize blueberry production. We aimed (i) to characterize the nutrient contents of healthy plants of three blueberry varieties adapted to warm climates (Emerald, Jewel, and Biloxi) using a reference method for foliar analysis (inductively coupled plasma (ICP)) and a portable X-ray fluorescence (pXRF) spectrometer on fresh and dry leaves and (ii) to differentiate blueberry varieties based on their nutrient composition. Nutrient content was statistically compared per leaf moisture condition (fresh or dry) with ICP results and used to differentiate the varieties via the random forest algorithm. P and Zn contents (ICP) in leaves were different among varieties. Dry leaf results (pXRF) were strongly correlated with ICP results. Most nutrients determined using ICP presented good correlation with pXRF data (R² from 0.66 to 0.93). The three varieties were accurately differentiated by pXRF results (accuracy: 87%; kappa: 0.80). Predictions of nutrient contents based on dry leaves analyzed by pXRF outperformed those based on fresh leaves. This approach can also be applied to other crops to facilitate nutrient assessment in leaves. Full article

The sustainability of coffee production is a concern for producers around the world. To be sustainable, it is necessary to achieve satisfactory levels of coffee productivity and quality. Pests and diseases cause reduced productivity and can affect the quality of coffee beans. To ensure sustainability, producers need to monitor pests that can lead to substantial crop losses, such as the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae), which belongs to the Lepidoptera order and the Lyonetiidae family. This research aimed to use machine learning techniques and vegetation indices to remotely identify infestations of the coffee leaf miner in coffee-growing regions. Field assessments of coffee leaf miner infestation were conducted in September 2023. Aerial images were taken using remotely piloted aircraft to determine 13 vegetative indices with RGB (red, green, blue) images. The vegetation indices were calculated using ArcGis 10.8 software. A comprehensive database encompassing details of coffee leaf miner infestation, vegetation indices, and crop data. The dataset was divided into training and testing subsets. A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). Following hyperparameter tuning, the test subset was employed for model validation. Remarkably, both the SVM and SGD models demonstrated superior performance in estimating coffee leaf miner infestations, with kappa indices of 0.6 and 0.67, respectively. The combined use of vegetation indices and crop data increased the accuracy of coffee leaf miner detection. The RF model performed poorly, while the SVM and SGD models performed better. This situation highlights the challenges of tracking coffee leaf miner infestations in fields with varying ages of coffee plants, different cultivars, and other environmental variables. Full article

Cereal grain crops are used as main food and raw feed materials all over the world. Among cereal crops, wheat occupies a leading place as the most valuable crop. Harvesting is the most energy-intensive stage in wheat cultivation. Therefore, improving technologies and tools to reduce energy costs in this process is an urgent task. A new stripping and threshing unit for harvesting cereal crops has been developed, allowing the harvesting of grain at both full maturity and in the early stages of maturity, when the grain has an increased content of protein and amino acids and is a valuable raw feed material. The new unit consists of a stripping and threshing unit. The stripping unit consists of a stripping drum and stripping combs. The threshing unit contains replaceable decks that collide with the grain, separating it from the ear; an auger for transporting the heap to the unloading device; and a blade beater with a cut-off shield. Wheat grain in the early stages of maturity has a strong connection with the ear, as a result of which harvesting such grain can be energy-intensive and impractical. In this regard, the purpose of this research was to study the dynamics of changes in the energy intensity of the wheat grain harvesting process during ripening and to compare the energy intensity of the harvesting process with the new unit with the energy intensity of a combine harvester. The methodology is based on measuring torque on the shaft of the stripping and threshing unit. The results show that the power required for stripping by the new unit is reduced from 8–10 kW to 2–4 kW, which is 2.5–4 times lower. The difference in power values between harvesting at the hard wax ripeness stage and full ripeness is only 1–1.5 kW, indicating the feasibility of harvesting grain at this stage. Full article

Plant pathogens are an important agricultural problem, and early and rapid pathogen identification is critical for crop preservation. This work focuses on using fluorescence spectroscopy to characterize and compare healthy and fungal pathogen-infected wheat grains. The excitation–emission matrices of whole wheat grains were measured using a fluorescence spectrometer. The samples included healthy control samples and grains manually infected with Fusarium graminearum and Alternaria alternata fungi. The five distinct zones were identified by analyzing the location of the fluorescence peaks at each measurement. The zone centered at λ_em = 328/λ_ex= 278 nm showed an increase in intensity for grains infected with both pathogens during all periods of the experiment. Another zone with the center λ_em = 480/λ_ex = 400 nm is most interesting from the point of view of early diagnosis of pathogen development. A statistically significant increase of fluorescence for samples with F. graminearum is observed on day 1 after infection; for A. alternata, on day 2, and the fluorescence of both decreases to the control level on day 7. Moreover, shifts in the emission peaks from 444 nm to 452 nm were recorded as early as 2–3 h after infection. These results highlight fluorescence spectroscopy as a promising technique for the early diagnosis of fungal diseases in cereal crops. Full article

Buckwheat and other grains have become influential in sustainable agriculture and food security owing to climate change. However, subpar storage conditions can result in the deterioration of the nutritional value and active components of buckwheat, making storage quality a significant research subject. This study examined common buckwheat (CB) and Tartary buckwheat (TB) stored at 4 °C, 30 °C, and 55 °C from 0 to 6 months to assess storage quality and its relationship to the preservation of active components. The results of agglomerative hierarchical clustering (AHC) and principal component analysis (PCA) showed that as storage temperature and time increased, both CB and TB exhibited the following differences: significant alterations in color due to an increase in browning index (B.I.), higher acidity from accelerated acid production at high temperatures, and a decrease in total phenolics, flavonoid content, and antioxidant capacity due to thermal degradation of functional components. In the storage quality assessment, no alteration in microstructure or degradation in components was detected after exposure to all times and temperatures, and the content of the primary bioactive compound, rutin, was CB (16.57–27.81 mg/100 g d.w.) and TB (707.70–787.58 mg/100 g d.w.), demonstrating buckwheat’s resistance to microbial contamination. Storage temperature significantly impacts buckwheat’s quality and bioactive components, making it an important element in establishing a sustainable food supply chain. Full article

This review explores the transformative potential of precision agriculture and proximal sensing in revolutionizing crop management practices. By delving into the complexities of these cutting-edge technologies, it examines their role in mitigating the adverse impacts of agrochemical usage while bringing crop health monitoring to a high precision level. The review explains how precision agriculture optimizes production while safeguarding environmental integrity, thus offering a viable solution to both ecological and economic challenges arising from excessive agrochemical application. Furthermore, it investigates various proximal sensing techniques, including spectral imaging, thermal imaging, and fluorescence sensors, showcasing their efficacy in detecting and diagnosing crop health indicators such as stress factors, nutrient deficiencies, diseases, and pests. Through an in-depth analysis of relevant studies and successful practical applications, this review highlights that it is essential to bridge the gap between monitoring sensors and real-time decision-making and to improve image processing and data management systems to fully realize their potential in terms of sustainable crop management practices. Full article

To address the gap in research on the mechanical properties of peanuts in the harvesting period, the mechanical properties of peanut seedling vines, pods, and peduncles were studied during the peanut harvesting period. The moisture content of peanut pods, peduncles, and seedling vines was measured, yielding values of 36.03%, 66.76%, and 77.95%, respectively. The tensile characteristic parameters between the pods and peduncles, peduncles and peduncles, and the seedling vines and peduncles during the harvesting period were determined using an electronic universal testing machine at three different loading speeds: 10 mm/min, 20 mm/min, and 30 mm/min. The test results indicated that the peduncle–peduncle, peduncle–pod, and peduncle–seedling vine detachment forces were 19.91 N, 17.41 N, and 8.62 N. The mechanical properties of peanuts during the harvesting period differed from those of peanuts that were excavated and sun-dried. Peanut-digging and turning machines should be designed based on the detachment force required to separate the peduncles from the pods, which, at a loading speed of 20 mm/min, is 17 N. This examination of the mechanical properties of peanuts during the harvesting period could have significant practical implications and a lasting influence on enhancing the efficiency and quality of peanut harvesting, refining harvesting machinery design, and advancing agricultural modernization. Full article

Automated sorting of sweetpotatoes is necessary to reduce labor dependence and costs that are significant at today’s sweetpotato packing sheds. Although optical sorters have been widely adopted in commercial packing lines for many horticultural commodities, there remains an unmet need to develop dedicated technology for the automated grading and sorting of sweetpotatoes. Sorting mechanisms are the critical component that physically segregates products according to quality grades determined by a machine vision or imaging system. This study presents the new engineering prototypes and evaluation of three different pneumatically powered mechanisms for sorting sweetpotatoes online. Among the three sorters, the sorting mechanism, which employs a linear air cylinder to drive a paddle directly striking products, achieved the best overall accuracy and repeatability of 98% and 96.8%, respectively, at conveyor speeds of 4–12 cm/s. The sorter based on a rotary actuator also delivered decent accuracy and repeatability of 97.9% and 95.6%, respectively. The best-performing sorting mechanism was integrated with a machine vision system that graded sweetpotatoes based on size and surface defect conditions to separate graded sweetpotatoes into three quality categories. The errors of 0–1% due to the sorting process were obtained at conveyor speeds of 4–12 cm/s, confirming the efficacy of the manufactured sorting mechanisms. There was a declining trend with the conveyor speed in the performance of the sorting mechanisms when evaluated either in a standalone or integrated configuration. The proposed sorting mechanisms that are simple in construction and operation and of low cost are useful for developing a more full-fledged sorting system. More research is needed to enhance sorting performance and conduct extensive tests at higher conveyor speeds for practical application. Full article

Grain quality is largely driven by grain infrastructure (technology) and handling practices (application of knowledge on handling). The use of inappropriate infrastructure and inappropriate handling protocols poses food safety and health-related risks. This review provides evidence for the link between drying and storage operations in the context of preserving grain quality. The purpose of this study was to evaluate the close grain quality relationship between drying and storage, with an appraisal of operations in Africa. This study further benchmarked successful and scalable models in Africa to infer guidance for promotion of optimal and effective drying and storage initiatives. While open-sun drying is undoubtedly the most adopted approach to grain drying for the rural-poor farmers, this study revealed greater success in grain storage, especially with the breakthrough at the introduction and adoption of small-scale hermetic storage technologies. Upon assessment of the cob, WFP Zero Food Loss Initiative, and AflaSight models implemented in Rwanda and Uganda, this study suggests: (i) the adoption of system thinking; (ii) the use of sustainable approaches such as gender inclusion, sustainable financing options, and use of existing infrastructures along-side novel interventions; and (iii) enabling policies and political will as strategic pathways for successful implementation of improved grain-quality interventions during drying and storage. In the short term, grain handlers must develop appropriate grain management protocols during open-sun drying to limit the impact of drying-related grain quality deterioration. Consortia-based implementation of the three models evaluated in this review could improve grain quality, food security and safety, and market linkages with premium grain markets, fostering economic growth and transformation. Full article

Agricultural cargo vehicles are responsible for applying severe soil pressures. However, the ground straw cover can attenuate the loads applied by wheels to the soil surface. This research evaluated the effect of three tires, p1—Radial Very Flex, p2—Radial Improved Flex, and a p3—Bias Ply tire, on three amounts of straw on the soil surface (0, 15, and 30 Mg ha⁻¹). We adopted a completely randomized design (CRD) with a rigid surface for three replications for the total contact area and punctual area claws. The soil bin test verified the deformable surface, tread marks, and soil penetration resistance (SPR). The tire’s claw design determines its punctual contact area, and the construction model determines the total contact area. The contact area in the soil bin increased linearly due to a increase in straw covering, reducing sinkage; p2 to 30 Mg ha⁻¹ straw shows the most significant contact area, p1 and p3 showed no difference. A straw increase from 0 to 30 Mg ha⁻¹ increased the contact areas by 25.5, 38.0, and 20.0% for p1, p2, and p3, respectively. Compared to the rigid surface, the p1 and p3 contact areas in the soil bin increased 6.2, 6.8, and 7.8 times in bare soil, 15, and 30 Mg ha⁻¹; for p2, this increase was up to 4.2, 4.5, and 5.9 times on the same surfaces. Keeping the straw on the soil improves its physical quality by reducing the SPR, so the straw has a buffer function in the wheel–soil relationship. Full article

The significance of soil in the agricultural industry is profound, with healthy soil representing an important role in ensuring food security. In addition, soil is the largest terrestrial carbon sink on earth. The soil carbon pool is composed of both inorganic and organic forms. The equilibrium of the soil carbon pool directly impacts the carbon cycle via all of the other processes on the planet. With the development of agricultural systems from traditional to conventional ones, and with the current era of precision agriculture, which involves making decisions based on information, the importance of understanding soil is becoming increasingly clear. The control of microenvironment conditions and soil fertility represents a key factor in achieving higher productivity in these systems. Furthermore, agriculture represents a significant contributor to carbon emissions, a topic that has become timely given the necessity for carbon neutrality. In addition to these concerns, updating soil-related data, including information on macro and micronutrient conditions, is important. Carbon represents one of the major nutrients for crops and plays a key role in the retention and release of other nutrients and the management of soil physical properties. Despite the importance of carbon, existing analytical methods are complex and expensive. This discourages frequent analyses, which results in a lack of soil carbon-related data for agricultural fields. From this perspective, in situ soil organic carbon (SOC) analysis can provide timely management information for calibrating fertilizer applications based on the soil–carbon relationship to increase soil productivity. In addition, the available data need frequent updates due to rapid changes in ecosystem services and the use of extensive fertilizers and pesticides. Despite the importance of this topic, few studies have investigated the potential of image analysis based on image processing and spectral data recording. The use of spectroscopy and visual color matching to develop SOC predictions has been considered, and the use of spectroscopic instruments has led to increased precision. Our extensive literature review shows that color models, especially Munsell color charts, are better for qualitative purposes and that Cartesian-type color models are appropriate for quantification. Even for the color model, spectroscopy data could be used, and these data have the potential to improve the precision of measurements. On the other hand, mid-infrared radiation (MIR) and near-infrared radiation (NIR) diffuse reflection has been reported to have a greater ability to predict SOC. Finally, this article reports the availability of inexpensive portable instruments that can enable the development of in situ SOC analysis from reflection and emission information with the integration of images and spectroscopy. This integration refers to machine learning algorithms with a reflection-oriented spectrophotometer and emission-based thermal images which have the potential to predict SOC without the need for expensive instruments and are easy to use in farm applications. Full article