AgriEngineering

Supplying high-quality fresh sweetpotato roots to the consumer requires sorting the roots by quality and removing culls deemed unsuitable for fresh markets at packing facilities. The sorting operation is traditionally performed by manual labor. This study surveyed the sorting lines of seven commercial sweetpotato packinghouses in Mississippi during the packing season of 2021. Sorting for defects entirely relied on labor, which accounted for up to 50% of the total labor in packinghouses. A cost–benefit analysis was conducted to determine the cost-effectiveness of implementing automated sorting technology as an alternative to manual sorting. The net benefits of automated sorting depended on labor savings and equipment costs. Machines at or less than USD 100,000 were economically beneficial with payback periods of less than three years when four or more workers could be replaced, while machines of USD 350,000 and higher would be not justifiable when quick economical returns were sought. Automated sorting promises to increase the profitability and competitiveness of fresh market sweetpotato packing industries. Full article

Agrometeorological models are great tools for predicting yields and improving decision-making. High-quality climatic data are essential for using these models. However, most developing countries have low-quality data with low frequency and spatial coverage. In this case, two main options are available: gathering more data in situ, which is expensive, or using gridded data, obtained from several sources. The main objective here was to evaluate the quality of two gridded climatic databases for filling gaps of real weather stations in the context of developing agrometeorological models. Therefore, a comparative analysis of gridded database and INMET data (precipitation and air temperature) was conducted using an agrometeorological model for sweet orange yield estimation. Both gridded databases had high determination and concordance coefficients for maximum and minimum temperatures. However, higher errors and lower confidence coefficients were observed for precipitation data due to their high dispersion. BR-DWGD indicated more accurate results and correlations in all scenarios evaluated in relation to NasaPower, pointing out that BR-DWGD may be better at filling gaps and providing inputs to simulate attainable yield in the Brazilian citrus belt. Nevertheless, due to the BR-DWGD database’s geographical and temporal limitations, NasaPower is still an alternative in some cases. Additionally, when using NasaPower, it is recommended to use a measured precipitation source to improve prediction quality. Full article

Piling behavior (PB) is a common issue that causes negative impacts on the health, welfare, and productivity of the flock in poultry houses (e.g., cage-free layer, breeder, and broiler). Birds pile on top of each other, and the weight of the birds can cause physical injuries, such as bruising or suffocation, and may even result in death. In addition, PB can cause stress and anxiety in the birds, leading to reduced immune function and increased susceptibility to disease. Therefore, piling has been reported as one of the most concerning production issues in cage-free layer houses. Several strategies (e.g., adequate space, environmental enrichments, and genetic selection) have been proposed to prevent or mitigate PB in laying hens, but less scientific information is available to control it so far. The current study aimed to develop and test the performance of a novel deep-learning model for detecting PB and evaluate its effectiveness in four CF laying hen facilities. To achieve this goal, the study utilized different versions of the YOLOv6 models (e.g., YOLOv6t, YOLOv6n, YOLOv6s, YOLOv6m, YOLOv6l, and YOLOv6l relu). The objectives of this study were to develop a reliable and efficient tool for detecting PB in commercial egg-laying facilities based on deep learning and test the performance of new models in research cage-free facilities. The study used a dataset comprising 9000 images (e.g., 6300 for training, 1800 for validation, and 900 for testing). The results show that the YOLOv6l relu-PB models perform exceptionally well with high average recall (70.6%), [email protected] (98.9%), and [email protected]:0.95 (63.7%) compared to other models. In addition, detection performance increases when the camera is placed close to the PB areas. Thus, the newly developed YOLOv6l relu-PB model demonstrated superior performance in detecting PB in the given dataset compared to other tested models. Full article

Field imagery is an effective way to capture the state of the entire field; yet, current field inspection approaches, when accounting for image resolution and processing speed, using existent imaging systems, do not always enable real-time field inspection. This project involves the innovation of novel technologies by using an FPGA-based image processing (FIP) device that eliminates the technical limitations of the current agricultural imaging services available in the market and will lead to the development of a market-ready service solution. The FIP prototype developed in this study was tested in both a laboratory and outdoor environment by using a digital single-lens reflex (DSLR) camera and web camera, respectively, as the reference system. The FIP system had a high accuracy with a Lin’s concordance correlation coefficient of 0.99 and 0.91 for the DLSR and web camera reference system, respectively. The proposed technology has the potential to provide on-the-spot decisions, which in turn, will improve the compatibility and sustainability of different land-based systems. Full article

The aim of this note is to provide a quick overview of the installation and adjustment of an exclusively mechanical standalone automatic device that self-adjusts to weather changes to control the frequency and duration of the irrigation. The “hydraulic evapotranspiration multisensor” (HEM) is composed of a reduced evaporation pan with water, a magnet with a floater floating in the pan, a hydraulic device operated by a magnetic hydraulic valve that has the ability to adjust the frequency of irrigation, and a hydraulic system that returns water to the pan during each irrigation event through an adjustable dripper to replace the water lost due to the fact of evaporation. This note is particularly relevant for arid–semi-arid regions where agricultural production is fully dependent on irrigation. Full article

Bruise damage is one of the mechanical injuries that fresh produce can sustain during the postharvest supply chain. The study investigated the effect of drop impact levels, storage temperatures, and the storage period on the quality changes of cucumbers. A known mass ball was used to damage cucumbers once from three different drop heights (30, 60, and 90 cm) before they were stored for 24 days at 5 °C, 10 °C, and 22 °C. The data showed that the bruise area (BA), bruise susceptibility (BS), yellowness, and chroma* increased with the increase in the drop height and storage temperature. The study found that the bruise area (BA) and bruise susceptibility (BS) of the damaged cucumbers increased substantially (p < 0.05) with the increase in storage temperature and drop height. Due to the increment in drop height, storage temperature, and the storage period, the weight loss (Wl)% significantly increased after 24 days of storage. The storage period affects the firmness of damaged cucumbers stored in all storage conditions. The highest value of lightness (L*) was observed for the cucumbers bruised from the 60 cm drop height and stored at 22 °C with a value of 43.08 on day 24 of storage. Hue*, redness (a*), and total soluble solids (TSS) were all unaffected by the drop height. This study can serve as a resource for horticultural researchers and experts involved in the fresh fruit and vegetable supply chain. The study pays attention to the importance of postharvest supply chain activities, such as handling and storage to maintain the quality and prolong the shelf life of perishable produce, such as cucumbers. Full article

Remote sensing technology applied to agricultural crops has emerged as an efficient tool to speed up the data acquisition process in decision-making. In this study, we aimed to evaluate the performance of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge (NDRE) in estimating biomass accumulation in common bean crops. The research was conducted at the Federal University of Lavras, where the ANFC 9 cultivar was used in an area of approximately seven hectares, in a second crop, in 2022. A total of 31 georeferenced points spaced at 50 m were chosen to evaluate height, width and green biomass, with collections on days 15, 27, 36, 58, 62 and 76 of the crop cycle. The images used in the study were obtained from the PlanetScope CubeSat satellite, with a spatial resolution of 3 m. The data obtained were subjected to a Pearson correlation (R) test and multiple linear regression analysis. The green biomass variable was significantly correlated with plant height and width. The NDVI performed better than the NDRE, with higher values observed at 62 Days After Sowing (DAS). The model that integrates the parameters of height, width and NDVI was the one that presented the best estimate for green biomass in the common bean crop. The M1 model showed the best performance to estimate green biomass during the initial stage of the crop, at 15, 27 and 36 DAS (R² = 0.93). These results suggest that remote sensing technology can be effectively applied to assess biomass accumulation in common bean crops and provide accurate data for decision-makers. Full article

Including the correct combination of factors for the application technology of pesticides can improve their distribution on their targets. The aim of this work was to use multivariate analysis to study the effect size and the order of influence of three factors that interfere with pesticide application technology in corn crops. A 2 × 2 × 3 factorial experiment was conducted with two droplet size classes (fine and coarse), two application rates (80 and 150 L ha⁻¹), and the presence of adjuvants (mineral oil one and two, and no adjuvant). A knapsack boom sprayer was used for the applications. Droplet deposition on the corn leaves was evaluated by detecting a tracer added to the spray via spectrophotometry and the droplet spectrum by analyzing water-sensitive papers. Univariate and multivariate statistical analyses were performed to integrate the variables analyzed. Droplet size has proven to be the most important factor in spraying planning, and the second factor is the application rate. With the association between fine droplets and higher application rates, a better performance was obtained in coverage, droplet density, and droplet deposition. Full article

Machine Learning (ML) has enabled many image-based object detection and recognition-based solutions in various fields and is the state-of-the-art method for these tasks currently. Therefore, it is of interest to apply this technique to different questions. In this paper, we explore whether it is possible to classify apple cultivars based on fruits using ML methods and images of the apple in question. The goal is to develop a tool that is able to classify the cultivar based on images that could be used in the field. This helps to draw attention to the variety and diversity in fruit growing and to contribute to its preservation. Classifying apple cultivars is a certain challenge in itself, as all apples are similar, while the variety within one class can be high. At the same time, there are potentially thousands of cultivars indicating that the task becomes more challenging when more cultivars are added to the dataset. Therefore, the first question is whether a ML approach can extract enough information to correctly classify the apples. In this paper, we focus on the technical requirements and prerequisites to verify whether ML approaches are able to fulfill this task with a limited number of cultivars as proof of concept. We apply transfer learning on popular image processing convolutional neural networks (CNNs) by retraining them on a custom apple dataset. Afterward, we analyze the classification results as well as possible problems. Our results show that apple cultivars can be classified correctly, but the system design requires some extra considerations. Full article

The Green Revolution led to an increased use of synthetic pesticides, causing environmental pollution. As an alternative, biopesticides made from entomopathogenic agents such as fungi have been sought. This study aimed to design and evaluate the performance of a harvester machine for efficiently collecting entomopathogenic spores of Metarhizium anisopliae and Beauveria bassiana grown on rice and corn substrates. The spore yield was estimated, and a spore count and a colony-forming unit (CFU) count were performed. Statistical analysis was conducted to compare the mean values of spores obtained from different combinations of solid substrate and fungi. The Corn-Metarhizium combination produced 34.15 g of spores per kg of substrate and 1.51 × 10⁹ CFUs mL⁻¹. Similarly, the Rice-Metarhizium combination produced 57.35 g per kg and 1.59 × 10⁹ CFUs mL⁻¹. Meanwhile, the Corn-Beauveria combination yielded 35.47 g per kg and 1.00 × 10⁹ CFUs mL⁻¹, while the Rice-Beauveria combination had a yield of 38.26 g per kg and 4.50 × 10⁸ CFUs mL⁻¹. Based on the reported results, the Rice-Metarhizium combination appears to be the most effective, yielding the highest number of harvested spores per kg of substrate. The study estimated a total cost of approximately $409.31 for manufacturing the harvester, considering only the cost of the materials. These results could potentially increase the availability and affordability of entomopathogenic fungi in integrated pest management. Full article

Developing different robotic platforms for farm operations is vital to addressing the increasing world population. A harvesting robot significantly increases a farm’s productivity while farmers focus on other relevant farm operations. From the literature, it could be summarized that the design concepts of the harvesting mechanisms were categorized as grasping and cutting, vacuum suction plucking systems, twisting and plucking mechanisms, and shaking and catching. Meanwhile, robotic system components include the mobile platform, manipulators, and end effectors, sensing and localization, and path planning and navigation. The robotic system must be cost-effective and safe. The findings of this research could contribute to the design process of developing a harvesting robot or developing a harvesting module that can be retrofitted to a commercially available mobile platform. This paper provides an overview of the most recent harvesting robots’ different concept designs and system components. In particular, this paper will highlight different agricultural ground mobile platforms and their associated mechanical design, principles, challenges, and limitations to characterize the crop environment relevant to robotic harvesting and to formulate directions for future research and development for cotton harvesting platforms. Full article

Deferred grazing (DG) consists in adapting the number of animals and the number of days grazed to the availability of pasture. Compared to continuous grazing (CG), which is based on a permanent and low stocking rate, DG is a management strategy that aims at optimizing the use of the resources available in the Mediterranean Montado ecosystem. This study with sheep grazing, carried out between 2019 and 2021 on a 4 ha pasture in Alentejo region of the Southern of Portugal, assesses the impact of these two grazing management systems on soil compaction as a result of animal trampling. This area of native natural grassland (a dryland pasture, mixture of grasses, legumes, and composite species) was divided into four grazing parks of 1 ha each, two under DG management and two under CG management. At the end of the study, the cone index (CI, in kPa) was measured in the topsoil layer (0–30 cm) with an electronic cone penetrometer at 48 georeferenced areas (12 in each park). The results of CI measurement showed no significant differences between treatments in all depths measured (0–10, 10–20, and 20–30 cm). These findings are encouraging from the point of view of soil conservation and sustainability, revealing good prospects for the intensification of extensive livestock production. Future work should evaluate the long-term impact and consider, at the same time, other ecosystem services and system productivity indicators. Full article

Single-frame circulation aquaculture belongs to the important category of sustainable agriculture development. In light of the visual-detection problem related to survival rate of Portunus in single-frame three-dimensional aquaculture, a fusion recognition algorithm based on YOLOV5, RCN (RefineContourNet) image recognition of residual bait ratio, centroid moving distance, and rotation angle was put forward. Based on three-parameter identification and LWLR (Local Weighted Linear Regression), the survival rate model of each parameter of Portunus was established, respectively. Then, the softmax algorithm was used to obtain the classification and judgment fusion model of Portunus’ survival rate. In recognition of the YOLOV5 residual bait and Portunus centroid, the EIOU (Efficient IOU) loss function was used to improve the recognition accuracy of residual bait in target detection. In RCN, Portunus edge detection and recognition, the optimized binary cross-entropy loss function based on double thresholds successfully improved the edge clarity of the Portunus contour. The results showed that after optimization, the mAP (mean Average Precision) of YOLOV5 was improved, while the precision and mAP (threshold 0.5:0.95:0.05) of recognition between the residual bait and Portunus centroid were improved by 2% and 1.8%, respectively. The loss of the optimized RCN training set was reduced by 4%, and the rotation angle of Portunus was obtained using contour. The experiment shows that the recognition accuracy of the survival rate model was 0.920, 0.840, and 0.955 under the single parameters of centroid moving distance, residual bait ratio, and rotation angle, respectively; and the recognition accuracy of the survival rate model after multi-feature parameter fusion was 0.960. The accuracy of multi-parameter fusion was 5.5% higher than that of single-parameter (average accuracy). The fusion of multi-parameter relative to the single-parameter (average) accuracy was a higher percentage. Full article

Drought has been known to be a natural hazard reflecting geographic and climatic characteristics. Satellite technology advancements have benefited drought assessment and monitoring to formulate plans for dealing with this slow-onset disaster. However, combining satellite remote sensing (RS) and meteorological data for drought monitoring is lacking in the literature. This study uses satellite RS and meteorological-based drought indicators to assess drought risk in the Ilocos Norte, Philippines. Data analysis included the retrieval of vegetation conditions using Sentinel-1 and Sentinel-2 data. The standardized precipitation index (SPI) and Keetch–Byram drought index (KBDI) were calculated to account for climatic variabilities. Results revealed that the Sentinel-1 backscatter coefficient decreased by −2 dB in the cropland area, indicating crop growth irregularities compared to grassland areas. These irregularities were supported by Sentinel-2 normalized difference vegetation index (NDVI) strong fluctuations during the two-year observation period. A significant coefficient of determination (R² > 0.60) between the Sentinel-1 backscatter coefficient and Sentinel-2 NDVI was observed for the study area. On the one hand, only KBDI significantly correlated (R² > 0.60) with the cropland area’s RS data-derived drought indicators. These results revealed RS data variability for drought risk management but are still valuable for developing an early warning system. Full article

Currently, Brazil is the leading producer of sugarcane in the world, with self-sufficiency in the use of ethanol as a biofuel, as well as being one of the largest suppliers of sugar to the world. This study aimed to develop a predictive model for sugarcane production based on data extracted from aerial imagery obtained from drones or satellites, allowing the precise tracking of plant development in the field. A model based on a semiparametric approach associated with the inverse Gaussian distribution applied to vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI), was developed with data from drone images obtained from two field experiments with randomized replications and four sugarcane varieties. These experiments were performed under conditions identical to those applied by sugarcane farmers. Further, the model validation was carried out by scaling up the analyses with data extracted from Sentinel-2 images of several commercial sugarcane fields. Very often, in countries such as Brazil, sugarcane crops occupy extensive areas. Consequently, the development of tools capable of being operated remotely automatically benefits the management of this crop in the field by avoiding laborious and time-consuming sampling and by promoting the reduction of operation costs. The results of the model application in both sources of data, i.e., data from field experiments as well as the data from commercial fields, showed a suitable level of overlap between the data of predicted yield using VIs generated from drone and satellite images with the data of verified yield obtained by measuring the production of experiments and commercial fields, indicating that the model is reliable for forecasting productivity months before the harvest time. Full article

The difference in the matrix present in soil samples from different areas limits the performance of nutrient analysis via XRF sensors, and only a few strategies to mitigate this effect to ensure an accurate analysis have been proposed so far. In this context, this research aimed to compare the performance of different predictive models, including a simple linear regression (RS), multiple linear regression (MLR), partial least-squares regression (PLS), and random forest (RF) models for the prediction of Ca and K in agricultural soils. RS models were evaluated on XRF data without (RS1) and with (RS2) Compton normalization. In addition, it was assessed whether using soil texture information and/or vis–NIR spectra as auxiliary variables would optimize the predictive performance of the models. The results showed that all strategies allowed the mitigation of the matrix effect to some degree, enabling the determination of their Ca and K contents with excellent predictive performance (R² ≥ 0.84). The best performance was obtained using RS2 for the Ca prediction (R² = 0.92, RSME = 48.25 mg kg⁻¹ and relative improvement (RI) of 52.3% compared to RS1) and using an RF for the K prediction (R² = 0.84, RSME = 17.43 mg kg⁻¹ and RI of 24.3% compared to RS1). The results indicated that sophisticated models did not always perform better than linear models. Furthermore, using texture data and vis–NIR spectra as auxiliary data was promising only for the K prediction, which showed an error reduction in the order of 10%, contrasting with the Ca prediction, which did not reduce the prediction error by more than 1%. The best modeling approach in our study proved to be attribute-specific. These results give further insight into the development of intelligence modeling strategies for sensor-based soil analysis. Full article

Recently, Panama wilt disease that attacks banana leaves has caused enormous economic losses to farmers. Early detection of this disease and necessary preventive measures can avoid economic damage. This paper proposes an improved method to predict Panama wilt disease based on symptoms using an agro deep learning algorithm. The proposed deep learning model for detecting Panama wilts disease is essential because it can help accurately identify infected plants in a timely manner. It can be instrumental in large-scale agricultural operations where Panama wilts disease could spread quickly and cause significant crop loss. Additionally, deep learning models can be used to monitor the effectiveness of treatments and help farmers make informed decisions about how to manage the disease best. This method is designed to predict the severity of the disease and its consequences based on the arrangement of color and shape changes in banana leaves. The present proposed method is compared with its previous methods, and it achieved 91.56% accuracy, 91.61% precision, 88.56% recall and 81.56% F1-score. Full article

Tank mixing of pesticides, a common practice in many countries, when performed incorrectly, can negatively impact the effectiveness of the pesticides. This study aimed to investigate the physical–chemical properties, droplet spectrum, and absorption by soybean plants of mixtures of the azoxystrobin fungicide with glyphosate herbicide and different adjuvants (mineral oil, propionic acid, and orange oil). The study design was completely randomized, with five treatments (T): T1, only fungicide; T2, fungicide + glyphosate; T3, fungicide + mineral oil; T4, fungicide + propionic acid; and T5, fungicide + orange oil. The spray concentrations simulated an application rate of 160 L ha⁻¹ using the TT110015 nozzle. The physical stability, hydrogen ion potential, electrical conductivity, surface tension, droplet spectrum, and azoxystrobin absorption in soybean plants were evaluated. To measure absorption, soybean plants were sprayed at the reproductive growth stage, and leaf samples were collected after 0, 2, and 48 h. The amount of azoxystrobin absorbed was determined using gas chromatography. The results indicated that the spray mixtures were physically compatible. All mixtures produced a medium droplet spectrum. T2 had the lowest absorption percentages, suggesting that these pesticides should not be mixed. Adjuvants increased the amount of azoxystrobin absorbed by the plants, and it took 2 h on average for the soybean leaves to absorb 72.58% of the applied spray. Full article

The level of garlic consumption in Indonesia increases as the population grows. This is because most of the ingredients of Indonesian food recipes contain garlic. However, local garlic production is not sufficient to fulfil the demand. Therefore, the Indonesian government imported garlic from other countries to fulfil the demand. To reduce the import capacity of garlic, the government made a regulation to increase the potential area for garlic cultivation in several priority locations in Indonesia, one of which is Sembalun District, East Lombok. To support government regulation, this study presents an application of machine learning and a statistic approach for the garlic field mapping method in Sembalun, Indonesia. This study comprises several steps including the Sentinel-1A images data acquisition, image preprocessing, machine learning and statistic model training, and model evaluation. k-nearest neighbor (k-NN) and maximum likelihood classification (MLC) methods are selected in this study. The performance of k-NN and MLC are compared to other garlic field classification results developed in previous studies using pixel-based and image-based classifications. The comparison results show that the k-NN classification is slightly better than the SVM classification and also that it outperformed the MLC method. In addition, MLC works faster than k-NN in learning the dataset and testing the models. The classification results can be used to estimate garlic production in the study area. The study concludes that the proposed methods are better than other classification models and the statistic approach. The future study will improve dataset quality to increase the model’s accuracy. Full article