Journal Description
AgriEngineering
AgriEngineering
is an international, peer-reviewed, open access journal on the engineering science of agricultural and horticultural production, published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), PubAg, FSTA, AGRIS, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Agricultural Engineering) / CiteScore - Q1 (Horticulture)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 25.8 days after submission; acceptance to publication is undertaken in 5.5 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.0 (2023);
5-Year Impact Factor:
3.1 (2023)
Latest Articles
A New Siamese Network Loss for Cattle Facial Recognition in a Few-Shot Learning Scenario
AgriEngineering 2024, 6(3), 2941-2954; https://doi.org/10.3390/agriengineering6030169 (registering DOI) - 20 Aug 2024
Abstract
This study explores the use of a Siamese neural network architecture to enhance classification performance in few-shot learning scenarios, with a focus on bovine facial recognition. Traditional methodologies often require large datasets, which can significantly stress animals during data collection. In contrast, the
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This study explores the use of a Siamese neural network architecture to enhance classification performance in few-shot learning scenarios, with a focus on bovine facial recognition. Traditional methodologies often require large datasets, which can significantly stress animals during data collection. In contrast, the proposed method aims to reduce the number of images needed, thereby minimizing animal stress. Systematic experiments conducted on datasets representing both full and few-shot learning scenarios revealed that the Siamese network consistently outperforms traditional models, such as ResNet101. It achieved notable improvements, with mean values increasing by over 6.5% and standard deviations decreasing by at least 0.010 compared to the ResNet101 baseline. These results highlight the Siamese network’s robustness and consistency, even in resource-constrained environments, and suggest that it offers a promising solution for enhancing model performance with fewer data and reduced animal stress, despite its slower training speed.
Full article
(This article belongs to the Section Livestock Farming Technology)
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Open AccessArticle
Use of Conyza canadensis L. Extracts as Biostimulant in Cyclamen persicum Mill.
by
Eunice R. Batista, Andre May, Sergio O. Procópio, Marcia R. Assalin, Helio D. Quevedo, Nicole Binhardi and Sonia C. N. Queiroz
AgriEngineering 2024, 6(3), 2926-2940; https://doi.org/10.3390/agriengineering6030168 - 16 Aug 2024
Abstract
Cyclamen (Cyclamen persicum Mill.) is an ornamental plant that is highly susceptible to pathogens, requiring high amounts of phytosanitary products. Therefore, the development of more sustainable alternatives has been required. The present study aimed to analyze the effect of C. canadensis root
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Cyclamen (Cyclamen persicum Mill.) is an ornamental plant that is highly susceptible to pathogens, requiring high amounts of phytosanitary products. Therefore, the development of more sustainable alternatives has been required. The present study aimed to analyze the effect of C. canadensis root extract (aqueous and with dichloromethane) applied via foliar or soil, in C. persicum, on gas exchange and the SPAD index and on the biomass of cyclamen. The aqueous extract treatment increased net CO2 assimilation, the transpiration rates, and instantaneous carboxylation efficiency. The water use efficiency values were reduced in the treatments with both extracts. The greatest increases in the SPAD index were provided by the aqueous extract. The cyclamens that received the aqueous extract applied in soil or the dichloromethane extract applied in leaves showed an increase in total biomass and number of leaves. To identify the compounds present in the extracts, CG-MS and LC-MS/MS analyses were performed. The positive effects obtained indicated a high biostimulant effect of C. Canadensis. Thus, the root extracts of C. Canadensis, particularly the aqueous extracts, have the potential to be used to reduce the use of mineral fertilizers and pesticides, promoting agroecological practices and contributing to sustainable agriculture.
Full article
(This article belongs to the Special Issue Sustainable Development of Agroecosystems: Advances in Agricultural Engineering)
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Open AccessSystematic Review
The Emerging Hemp Industry: A Review of Industrial Hemp Materials and Product Manufacturing
by
Dolor R. Enarevba and Karl R. Haapala
AgriEngineering 2024, 6(3), 2891-2925; https://doi.org/10.3390/agriengineering6030167 - 14 Aug 2024
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There is a growing need for resilient and renewable materials to aid society in global sustainability. It is incumbent upon the agricultural and manufacturing industries to work together to achieve this vision. In particular, the hemp plant has been identified as an emerging
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There is a growing need for resilient and renewable materials to aid society in global sustainability. It is incumbent upon the agricultural and manufacturing industries to work together to achieve this vision. In particular, the hemp plant has been identified as an emerging industrial crop that will be pivotal in achieving the United Nations Sustainable Development Goals. However, this nascent industry has received an influx of research and development activity, resulting in various methods and practices globally, challenging the repeatability of results, research advancement, standards development, and sustainability assessment. A systematic literature review is conducted to identify and document (1) the various practices for harvesting and converting industrial hemp into materials and products and (2) existing hemp-derived products and those under development. Using the PRISMA methodology, 5295 articles were identified, and 109 articles were included for review. Unlike prior reviews focusing on specific hemp plant components, materials, or products, this study systematically evaluates the utilization pathways of the whole plant (stalk, flower, leaf, and seed) to traditional, industrial, and emerging products. Further, myriad opportunities for hemp material and product applications, sustainability performance assessment, and future research are discussed. This review will benefit future hemp research, advancing process technologies, developing novel products, establishing policies and standards, and assessing sustainability performance.
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Graphical abstract
Open AccessArticle
Optimizing Orchard Planting Efficiency with a GIS-Integrated Autonomous Soil-Drilling Robot
by
Osman Eceoğlu and İlker Ünal
AgriEngineering 2024, 6(3), 2870-2890; https://doi.org/10.3390/agriengineering6030166 - 13 Aug 2024
Abstract
A typical orchard’s mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the
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A typical orchard’s mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the planting hole is the most time-consuming operation. In fruit orchards, the use of robots is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can be highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GISs), to develop an autonomous drilling machine and use the developed robot to open planting holes. There is no comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four=wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with a reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS 2020 software was used for designing and drawing the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards.
Full article
(This article belongs to the Special Issue Advancing Smart Farming through Agricultural Robots and Automation Technologies)
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Open AccessArticle
Machine-Learning Microclimate Forecasting for Adaptive Equipment Control via Web Integration in Open-Ventilated Greenhouses
by
Kyaw Maung Maung Thwin, Teerayut Horanont and Teera Phatrapornnant
AgriEngineering 2024, 6(3), 2845-2869; https://doi.org/10.3390/agriengineering6030165 - 13 Aug 2024
Abstract
Open-ventilated greenhouses have reasonable setup costs and low operational costs for growers, which is crucial and most appealing for this research. These attributes fit developing nations like Thailand and other tropical regions. It is challenging to control the equipment intended to obtain an
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Open-ventilated greenhouses have reasonable setup costs and low operational costs for growers, which is crucial and most appealing for this research. These attributes fit developing nations like Thailand and other tropical regions. It is challenging to control the equipment intended to obtain an ideal microclimate. This research was conducted in an actual greenhouse setting for data collection and experiments, with a proposed system for adaptive equipment control via web integration. Also, the proposed multivariate multistep LSTM was forecasted over 1 h and cooperated with sensor data. Additional sensors, like a leaf wetness sensor and a CO2 sensor, were installed for detecting plant-level precision for vaporization, rather than greenhouse-level. The proposed system can optimize the indoor temperature within 34.5 to 36 °C with a 39 to 40 °C outdoor temperature. Also, humidity was still at the ideal level of 68 to 70%; more precisely, the wetness value was below 300 throughout the experiment. The model accuracy achieved a sufficient RMSE (0.49) and R2 (0.9788). This proposed system architecture and MM-LSTM model has potential as one dimension of a fully smart greenhouse system development in open-ventilated greenhouse settings in tropical regions and Southeast Asian nations for a better yield rate and less human interaction.
Full article
(This article belongs to the Section Computer Applications and Artificial Intelligence in Agriculture)
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Open AccessArticle
Augmented Reality Applied to Identify Aromatic Herbs Using Mobile Devices
by
William Aparecido Celestino Lopes, João Carlos Lopes Fernandes, Samira Nascimento Antunes, Marcelo Eloy Fernandes, Irenilza de Alencar Nääs, Oduvaldo Vendrametto and Marcelo Tsuguio Okano
AgriEngineering 2024, 6(3), 2824-2844; https://doi.org/10.3390/agriengineering6030164 - 13 Aug 2024
Abstract
Correctly identifying and classifying food is decisive in food safety. The food sector is constantly evolving, and one of the technologies that stands out is augmented reality (AR). During practical studies at Companhia de Entreposto e Armazéns Gerais de São Paulo (CEAGESP), responsible
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Correctly identifying and classifying food is decisive in food safety. The food sector is constantly evolving, and one of the technologies that stands out is augmented reality (AR). During practical studies at Companhia de Entreposto e Armazéns Gerais de São Paulo (CEAGESP), responsible for the largest food storage in South America, difficulties were identified in classifying aromatic herbs due to the large number of species. The project aimed to create an innovative AR application called ARomaticLens to solve the challenges associated with identifying and classifying aromatic herbs using the design science research (DSR) methodology. The research was divided into five stages according to the DSR methodology, from surveying the problem situation at CEAGESP to validating the application through practical tests and an experience questionnaire carried out by CEAGESP specialists. The result of the study presented 100% accuracy in identifying the 18 types of aromatic herbs studied when associated with the application’s local database without the use of an Internet connection, in addition to a score of 8 on a scale of 0 to 10 in terms of the usability of the interface as rated by users. The advantage of the applied method is that the app can be used offline.
Full article
(This article belongs to the Special Issue Computer Vision for Agriculture and Smart Farming)
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Open AccessArticle
Typology of Production Units for Improving Banana Agronomic Management in Ecuador
by
Carlos Alberto Quiloango-Chimarro, Henrique Raymundo Gioia and Jéfferson de Oliveira Costa
AgriEngineering 2024, 6(3), 2811-2823; https://doi.org/10.3390/agriengineering6030163 - 12 Aug 2024
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Ecuador is one of the world’s leading banana exporters; however, low productivity resulting from inadequate agronomic management requires an analysis of banana production units. This study aimed to define the types of banana production units based on the different agronomic management practices adopted
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Ecuador is one of the world’s leading banana exporters; however, low productivity resulting from inadequate agronomic management requires an analysis of banana production units. This study aimed to define the types of banana production units based on the different agronomic management practices adopted by producers in two Ecuadorian provinces. Data from the National Institute of Statistics and Censuses (INEC) for 2021 were used, with a sample of 319 production units. Principal component and cluster analyses were applied to identify the different types of production units, resulting in four types: high technology conventional (Cluster 1), balanced conventional (Cluster 2), intensive conventional (Cluster 3), and agroecological (Cluster 4). It is important to highlight that 58% of the production units are intensive conventional and use an average of 3.5 management practices, with 98% using fertilizers, 100% using fungicides and pesticides, and 45% using improved genotypes. In contrast, agroecological production is still incipient in Ecuador (4.7%). Regression analysis showed that waste is important in high-yield production units in the three clusters. In addition, Cluster 2 relied on regional factors, family labor, and irrigation efficiency, while in intensive conventional farms (Cluster 3), banana yield was related to fungicide application. Therefore, public policies should be customized according to cluster-specific characteristics to optimize agronomic management practices and facilitate their transfer among groups.
Full article
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Open AccessArticle
An Integrated Engineering Method for Improving Air Quality of Cage-Free Hen Housing
by
Ramesh Bahadur Bist, Xiao Yang, Sachin Subedi, Bidur Paneru and Lilong Chai
AgriEngineering 2024, 6(3), 2795-2810; https://doi.org/10.3390/agriengineering6030162 - 9 Aug 2024
Abstract
High particulate matter levels in cage-free (CF) houses have led to concerns from producers, as that can pose significant risks to the health and well-being of hens and their caretakers. This study aimed to assess the effectiveness of an electrostatic particle ionization (EPI)
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High particulate matter levels in cage-free (CF) houses have led to concerns from producers, as that can pose significant risks to the health and well-being of hens and their caretakers. This study aimed to assess the effectiveness of an electrostatic particle ionization (EPI) + bedding management (BM) treatment in reducing particulate matter (PM) concentrations. Four identical CF rooms each housed 175 hens for six weeks, with two rooms assigned to the EPI + BM treatment (EPI + 20% wood chip topping over 81-week-old litter) and the other two as controls. Measurements of PM were conducted twice a week for 10 min using TSI DustTrak. Additionally, small and large particle concentrations were monitored continuously using a Dylos monitor, with a sampling period of one minute. Footpad scoring was recorded for logistic analysis. Statistical analysis was performed using ANOVA with the Tukey HSD method (p < 0.05). Results demonstrated that the EPI + BM treatment significantly reduced particle counts (37.83% decrease in small particles, 55.90% decrease in large particles) compared to the control group (p < 0.01). PM concentrations were also substantially lowered across different size fractions, ranging from 58.41% to 64.17%. These findings underscore the effectiveness of the EPI + BM treatment in reducing PM in CF houses. The integration of EPI and bedding management innovated in this study holds promise for improving air quality and contributing to the well-being of hens and caretakers in CF housing systems.
Full article
(This article belongs to the Section Livestock Farming Technology)
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Open AccessArticle
Integrating Actuator Fault-Tolerant Control and Deep-Learning-Based NDVI Estimation for Precision Agriculture with a Hexacopter UAV
by
Gerardo Ortiz-Torres, Manuel A. Zurita-Gil, Jesse Y. Rumbo-Morales, Felipe D. J. Sorcia-Vázquez, José J. Gascon Avalos, Alan F. Pérez-Vidal, Moises B. Ramos-Martinez, Eric Martínez Pascual and Mario A. Juárez
AgriEngineering 2024, 6(3), 2768-2794; https://doi.org/10.3390/agriengineering6030161 - 8 Aug 2024
Abstract
This paper presents an actuator fault-tolerant control (FTC) strategy for a hexacopter unmanned aerial vehicle (UAV) designed specifically for precision agriculture applications. The proposed approach integrates advanced sensing techniques, including the estimation of Near-Infrared (NIR) reflectance from RGB imagery using the Pix2Pix deep
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This paper presents an actuator fault-tolerant control (FTC) strategy for a hexacopter unmanned aerial vehicle (UAV) designed specifically for precision agriculture applications. The proposed approach integrates advanced sensing techniques, including the estimation of Near-Infrared (NIR) reflectance from RGB imagery using the Pix2Pix deep learning network based on conditional Generative Adversarial Networks (cGANs), to enable the calculation of the Normalized Difference Vegetation Index (NDVI) for health assessment. Additionally, trajectory flight planning is developed to ensure the efficient coverage of the targeted agricultural area while considering the vehicle’s dynamics and fault-tolerant capabilities, even in the case of total actuator failures. The effectiveness of the proposed system is validated through simulations and real-world experiments, demonstrating its potential for reliable and accurate data collection in precision agriculture. An NDVI test was conducted on a sugarcane crop using the estimated NIR to assess the crop’s condition during its tillering stage. Therefore, the main contributions this paper include (i) the development of an actuator FTC strategy for a hexacopter UAV in precision agriculture applications, integrating advanced sensing techniques such as NIR reflectance estimation using deep learning network; (ii) the design of a flight trajectory planning method ensuring the efficient coverage of the targeted agricultural area, considering the vehicle’s dynamics and fault-tolerant capabilities; (iii) the validation of the proposed system through simulations and real-world experiments; and (iv) the successful integration of FTC scheme, advanced sensing, and flight trajectory planning for reliable and accurate data collection in precision agriculture.
Full article
(This article belongs to the Special Issue Sensors and Actuators for Crops and Livestock Farming)
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Open AccessArticle
Optimizing Deep Learning Algorithms for Effective Chicken Tracking through Image Processing
by
Saman Abdanan Mehdizadeh, Allan Lincoln Rodrigues Siriani and Danilo Florentino Pereira
AgriEngineering 2024, 6(3), 2749-2767; https://doi.org/10.3390/agriengineering6030160 - 8 Aug 2024
Abstract
Identifying bird numbers in hostile environments, such as poultry facilities, presents significant challenges. The complexity of these environments demands robust and adaptive algorithmic approaches for the accurate detection and tracking of birds over time, ensuring reliable data analysis. This study aims to enhance
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Identifying bird numbers in hostile environments, such as poultry facilities, presents significant challenges. The complexity of these environments demands robust and adaptive algorithmic approaches for the accurate detection and tracking of birds over time, ensuring reliable data analysis. This study aims to enhance methodologies for automated chicken identification in videos, addressing the dynamic and non-standardized nature of poultry farming environments. The YOLOv8n model was chosen for chicken detection due to its high portability. The developed algorithm promptly identifies and labels chickens as they appear in the image. The process is illustrated in two parallel flowcharts, emphasizing different aspects of image processing and behavioral analysis. False regions such as the chickens’ heads and tails are excluded to calculate the body area more accurately. The following three scenarios were tested with the newly modified deep-learning algorithm: (1) reappearing chicken with temporary invisibility; (2) multiple missing chickens with object occlusion; and (3) multiple missing chickens with coalescing chickens. This results in a precise measure of the chickens’ size and shape, with the YOLO model achieving an accuracy above 0.98 and a loss of less than 0.1. In all scenarios, the modified algorithm improved accuracy in maintaining chicken identification, enabling the simultaneous tracking of several chickens with respective error rates of 0, 0.007, and 0.017. Morphological identification, based on features extracted from each chicken, proved to be an effective strategy for enhancing tracking accuracy.
Full article
(This article belongs to the Section Livestock Farming Technology)
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Open AccessArticle
Construction of a Discrete Elemental Model for Clayey Soil Considering Pressure–Sinkage Nonlinear Relationship to Investigate Stress Transfer
by
Zhuohuai Guan, Dong Jiang, Min Zhang, Haitong Li, Mei Jin and Tao Jiang
AgriEngineering 2024, 6(3), 2732-2748; https://doi.org/10.3390/agriengineering6030159 - 7 Aug 2024
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The discrete element method (DEM) has been extensively utilized to investigate the mechanical properties of granules, particularly their microscopic behavior, overcoming limitations in field tests such as cost, time consumption, and soil condition restrictions. To ensure the development of reliable DEM simulations, proper
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The discrete element method (DEM) has been extensively utilized to investigate the mechanical properties of granules, particularly their microscopic behavior, overcoming limitations in field tests such as cost, time consumption, and soil condition restrictions. To ensure the development of reliable DEM simulations, proper contact model selection and parameter calibration are essential. In this research, a DEM parameter calibration method that could represent the nonlinear relationship between clayey soil pressure and sinkage at different moisture contents was proposed. Firstly, the sinking modulus K and the soil deformation exponent n were identified to reflect the nonlinear pressure–sinkage relationship. Then, sensitive DEM parameters on the soli pressure–sinkage relationship were investigated and calibrated, and the effect of moisture content on them was explored. Finally, the transfer of soil internal stress during subsidence was analyzed using the constructed discrete element model. The average error of the sinking modulus K and the soil deformation exponent n between the DEM and the experimental result at four moisture contents were 4.7% and 4.9%, respectively. The relative error of soil internal stress between simulation and experiment was 6.7%, 4.4%, and 9.7% at depths of 50 mm, 100 mm, and 150 mm, respectively. The soil particle trajectory, soil internal stress distribution, and variations during plate pressure–sinkage progress were analyzed by the constructed DEM model. The results demonstrated good agreement with theoretical models and experimental findings. The proposed clayey soil DEM modeling process that considers the pressure–sinkage nonlinear relationship at different moisture contents can be applied in machine-soil research.
Full article
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Open AccessArticle
Integrating Aquaponics with Macrobrachium amazonicum (Palaemonidae: Decapoda) Cultivation for the Production of Microgreens: A Sustainable Approach
by
Sávio L. M. Guerreiro, João Francisco Garcez Cabral Júnior, Bruno J. C. F. Eiras, Bruna dos Santos Miranda, Priscila Caroline Alves Lopes, Nuno Filipe Alves Correia de Melo, Ronald Kennedy Luz, Fábio Carneiro Sterzelecki and Glauber David Almeida Palheta
AgriEngineering 2024, 6(3), 2718-2731; https://doi.org/10.3390/agriengineering6030158 - 7 Aug 2024
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The use of aquaponic systems has grown in recent years, but few of these systems have integrated the production of prawns and short-cycle vegetables. This study evaluated the potential for producing microgreens (beet, amaranth, arugula, and red cabbage) integrated with Amazon River prawns
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The use of aquaponic systems has grown in recent years, but few of these systems have integrated the production of prawns and short-cycle vegetables. This study evaluated the potential for producing microgreens (beet, amaranth, arugula, and red cabbage) integrated with Amazon River prawns (Macrobrachium amazonicum) in an aquaponic system. Four seeding densities (5, 10, 15, or 20 seeds/cell) were assessed in two treatments: one using prawn wastewater and the other using plain dechlorinated water (control). Water quality, prawn growth performance, and microgreen productivity were monitored over 13 days, revealing optimal conditions for both prawns and microgreens in the aquaponic system. Amaranthus paniculatus yielded 374.00 g/m2 in prawn wastewater compared to 231.34 g/m2 in the control, while Beta vulgaris produced 1734.39 g/m2 in wastewater versus 1127.69 g/m2 in the control. Similarly, Brassica oleracea (2180.69 g/m2) and Eruca sativa (2109.46 g/m2) had higher yields in the prawn aquaponics system. These findings demonstrate that integrating prawn cultivation in aquaponic systems significantly enhances microgreen production compared to traditional methods. This integrated approach not only improves yields but also offers a more sustainable production model. Significant variation in productivity and growth metrics among species treatments underscores the viability and need for more systematic aquaponic procedures.
Full article
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Open AccessSystematic Review
Comparison of Mango (Mangifera indica) Dehydration Technologies: A Systematic Review
by
Luna C. López and Gustavo Adolfo Hincapié-Llanos
AgriEngineering 2024, 6(3), 2694-2717; https://doi.org/10.3390/agriengineering6030157 - 6 Aug 2024
Abstract
The convective hot-air drying technology can cause physicochemical, nutritional, and organoleptic losses in the mango (Mangifera indica). The present Systematic Review was carried out with the objective of comparing mango dehydration technologies to identify the effects on the physicochemical, nutritional, and
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The convective hot-air drying technology can cause physicochemical, nutritional, and organoleptic losses in the mango (Mangifera indica). The present Systematic Review was carried out with the objective of comparing mango dehydration technologies to identify the effects on the physicochemical, nutritional, and organoleptic properties of the fruit. Through a review of published scientific and conference papers in the Scopus database, adjusted to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, a total of 134 documents dated between 2000 and December 6 of 2022 were obtained; 76 of these documents were finally included in the bibliographic and theoretical analysis. Selection parameters emphasizing the relationship between the articles and the research topic, evidenced by including at least one of three dehydration technologies and the fruit of interest with an experimental or theoretical approach to the dehydration subject; review articles and surveys were excluded. Correlation graphs of bibliographic variables were made using the data mining software VantagePoint (version 15.1), which was graphically restructured in Microsoft Excel with the support of statistical analysis. Of the resulting articles, it was found that the countries with authors who participated most in scientific production like India, Brazil, Colombia, the United States, and Thailand, were those related to mango production or importation. Furthermore, the freeze-drying technology allows operating at lower temperatures than convective hot-air drying, contributing to the preservation of ascorbic acid, among other compounds. The refractance window has the shortest operation time to obtain moisture values between 10 and 20%. The dehydrated samples using the refractance window are smooth, homogeneous, non-porous, and comparable to the color obtained with freeze-drying, which is acceptable for industrial applications.
Full article
(This article belongs to the Section Pre and Post-Harvest Engineering in Agriculture)
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Open AccessArticle
Subsoiler Tool with Bio-Inspired Attack Edge for Reducing Draft Force during Soil Tillage
by
Francisco Garibaldi-Márquez, Ernesto Martínez-Reyes, Cornelio Morales-Morales, Lenin Ramos-Cantú, Mirna Castro-Bello and Armida González-Lorence
AgriEngineering 2024, 6(3), 2678-2693; https://doi.org/10.3390/agriengineering6030156 - 5 Aug 2024
Abstract
To alleviate soil compaction, subsoiling practices using subsoiler implements are commonly implemented. However, subsoiler bodies are subjected to great draft forces because they work deep in the soil. Therefore, to contribute to draft force reduction, in this work, a bio-inspired attack edge for
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To alleviate soil compaction, subsoiling practices using subsoiler implements are commonly implemented. However, subsoiler bodies are subjected to great draft forces because they work deep in the soil. Therefore, to contribute to draft force reduction, in this work, a bio-inspired attack edge for a subsoiler body based on the internal and external contour lines of the claws of the Mexican ground squirrel (Spermophilus mexicanus) is proposed. As a first step, computational fluid dynamic (CFD) modeling was used to select the best bionic subsoiler (BS) according to the draft force requirements. Then, the BS was fabricated and field-evaluated, and its real draft force during tillage was contrasted with those of a curve subsoiler (CS) and a straight subsoiler (SS). The field evaluation demonstrated that the BS demands, on average, and less draft force than the CS and SS, respectively. Additionally, the BS was better at entering the soil since its mean tillage depths were and higher than those of the SS and CS geometries, respectively. Therefore, it was found that modeling the attacking edge of a subsoiler body after the Mexican ground squirrel clearly reduced the draft force during tillage.
Full article
(This article belongs to the Section Agricultural Mechanization and Machinery)
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Open AccessTechnical Note
On Developing a Machine Learning-Based Approach for the Automatic Characterization of Behavioral Phenotypes for Dairy Cows Relevant to Thermotolerance
by
Oluwatosin Inadagbo, Genevieve Makowski, Ahmed Abdelmoamen Ahmed and Courtney Daigle
AgriEngineering 2024, 6(3), 2656-2677; https://doi.org/10.3390/agriengineering6030155 - 5 Aug 2024
Abstract
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The United States is predicted to experience an annual decline in milk production due to heat stress of 1.4 and 1.9 kg/day by the 2050s and 2080s, with economic losses of USD 1.7 billion and USD 2.2 billion, respectively, despite current cooling efforts
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The United States is predicted to experience an annual decline in milk production due to heat stress of 1.4 and 1.9 kg/day by the 2050s and 2080s, with economic losses of USD 1.7 billion and USD 2.2 billion, respectively, despite current cooling efforts implemented by the dairy industry. The ability of cattle to withstand heat (i.e., thermotolerance) can be influenced by physiological and behavioral factors, even though the factors contributing to thermoregulation are heritable, and cows vary in their behavioral repertoire. The current methods to gauge cow behaviors are lacking in precision and scalability. This paper presents an approach leveraging various machine learning (ML) (e.g., CNN and YOLOv8) and computer vision (e.g., Video Processing and Annotation) techniques aimed at quantifying key behavioral indicators, specifically drinking frequency and brush use- behaviors. These behaviors, while challenging to quantify using traditional methods, offer profound insights into the autonomic nervous system function and an individual cow’s coping mechanisms under heat stress. The developed approach provides an opportunity to quantify these difficult-to-measure drinking and brush use behaviors of dairy cows milked in a robotic milking system. This approach will open up a better opportunity for ranchers to make informed decisions that could mitigate the adverse effects of heat stress. It will also expedite data collection regarding dairy cow behavioral phenotypes. Finally, the developed system is evaluated using different performance metrics, including classification accuracy. It is found that the YoloV8 and CNN models achieved a classification accuracy of 93% and 96% for object detection and classification, respectively.
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Open AccessArticle
Studying the Relationship between Satellite-Derived Evapotranspiration and Crop Yield: A Case Study of the Cauvery River Basin
by
Anish Anand, Venkata Reddy Keesara and Venkataramana Sridhar
AgriEngineering 2024, 6(3), 2640-2655; https://doi.org/10.3390/agriengineering6030154 - 5 Aug 2024
Abstract
Satellite-derived evapotranspiration (ETa) products serve global applications, including drought monitoring and food security assessment. This study examines the applicability of ETa data from two distinct sources, aiming to analyze its correlation with crop yield (rice, maize, barley, soybean). Given the critical role of
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Satellite-derived evapotranspiration (ETa) products serve global applications, including drought monitoring and food security assessment. This study examines the applicability of ETa data from two distinct sources, aiming to analyze its correlation with crop yield (rice, maize, barley, soybean). Given the critical role of crop yield in economic and food security contexts, monthly and yearly satellite-derived ETa data were assessed for decision-makers, particularly in drought-prone and food-insecure regions. Utilizing QGIS, zonal statistics operations and time series graphs were employed to compare ETa with crop yield and ET anomaly. Data processing involved converting NRSC daily data to monthly and extracting single-pixel ET data using R Studio. Results reveal USGSFEWS as a more reliable ETa source, offering better accuracy and data continuity, especially during monsoon seasons. However, the correlation between crop yield and ETa ranged from 12% to 35%, while with ET anomaly, it ranged from 35% to 55%. Enhanced collection of satellite-based ETa and crop-yield data is imperative for informed decision-making in these regions. Despite limitations, ETa can moderately guide decisions regarding crop-yield management.
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(This article belongs to the Special Issue Application of Geographic Information System and Remote Sensing Technology in Agricultural and Forestry Research)
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Open AccessArticle
GamaNNet: A Novel Plant Pathologist-Level CNN Architecture for Intelligent Diagnosis
by
Marcio Oliveira, Adunias Teixeira, Guilherme Barreto and Cristiano Lima
AgriEngineering 2024, 6(3), 2623-2639; https://doi.org/10.3390/agriengineering6030153 - 2 Aug 2024
Abstract
Plant pathologies significantly jeopardise global food security, necessitating the development of prompt and precise diagnostic methods. This study employs advanced deep learning techniques to evaluate the performance of nine convolutional neural networks (CNNs) in identifying a spectrum of phytosanitary issues affecting the foliage
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Plant pathologies significantly jeopardise global food security, necessitating the development of prompt and precise diagnostic methods. This study employs advanced deep learning techniques to evaluate the performance of nine convolutional neural networks (CNNs) in identifying a spectrum of phytosanitary issues affecting the foliage of Solanum lycopersicum (tomato). Ten thousand RGB images of leaf tissue were subsampled in training (64%), validation (16%), and test (20%) sets to rank the most suitable CNNs in expediting the diagnosis of plant disease. The study assessed the performance of eight well-known networks under identical hyperparameter conditions. Additionally, it introduced the GamaNNet architecture, a custom-designed model optimised for superior performance on this specific type of dataset. The investigational results were most promising for the innovative GamaNNet and ResNet-152, which both exhibited a 91% accuracy rate, as evidenced by their confusion matrices, ROC curves, and AUC metrics. In comparison, LeNet-5 and ResNet-50 demonstrated lower assertiveness, attaining accuracies of 74% and 69%, respectively. GoogLeNet and Inception-v3 emerged as the frontrunners, displaying diagnostic preeminence, achieving an average F1-score of 97%. Identifying such pathologies as Early Blight, Late Blight, Corynespora Leaf Spot, and Septoria Leaf Spot posed the most significant challenge for this class of problem.
Full article
(This article belongs to the Special Issue Recent Trends and Advances in Agricultural Engineering)
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Graphical abstract
Open AccessArticle
Biomass of Eichhornia crassipes as an Alternative Substrate for the Formation of Lettuce Seedlings
by
María Isabel Laguna-Estrada, Jorge Eric Ruiz-Nieto, Adolfo R. Lopez-Nuñez, Juan G. Ramírez-Pimentel, Juan Carlos Raya-Pérez and Cesar L. Aguirre-Mancilla
AgriEngineering 2024, 6(3), 2612-2622; https://doi.org/10.3390/agriengineering6030152 - 2 Aug 2024
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The production of lettuce has increased significantly due to the use of hydroponic systems that rely on substrates. Disposal and acquisition costs present problems, necessitating the identification of sustainable alternatives. The present study aimed to evaluate the use of Eichhornia crassipes (water hyacinth)
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The production of lettuce has increased significantly due to the use of hydroponic systems that rely on substrates. Disposal and acquisition costs present problems, necessitating the identification of sustainable alternatives. The present study aimed to evaluate the use of Eichhornia crassipes (water hyacinth) dry matter in a substrate for the formation of lettuce seedlings. Water plants were collected to obtain their dry matter, and twelve mixtures were formed with Sphagnum and perlite. Mixtures with more water hyacinth dry matter exhibited greater water retention. However, these mixtures also lost water at a faster rate than those containing primarily Sphagnum dry matter did. Higher percentages of germination were detected in the mixtures with water hyacinth dry matter, but these seedlings also presented higher concentrations of proline, such as 16.0 µg mL−1. The mixtures with water hyacinth dry matter presented the highest ion concentrations, mainly at high levels of humidity. Mixtures with a high proportion of water hyacinth dry matter had a greater water retention capacity and a high percentage of lettuce seed that germinated. The mixtures with a higher proportion of Sphagnum led to greater root length, greater concentrations of chlorophyll in cotyledonary leaves, and better morphological development of the seedlings.
Full article
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Open AccessArticle
Prediction of Soil Field Capacity and Permanent Wilting Point Using Accessible Parameters by Machine Learning
by
Liwei Liu and Xingmao Ma
AgriEngineering 2024, 6(3), 2592-2611; https://doi.org/10.3390/agriengineering6030151 - 2 Aug 2024
Abstract
The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this
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The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. The best-fit variable combination for FC (longitude, latitude, altitude, sand content, silt content, clay content, and electrical conductivity) and PWP (best-fit FC combination plus pH) modeling was determined. Both ANN and GEP showed greater accuracy than linear-based models in simulating the FC and PWP from the best-fit variables. The mean absolute error (MAE) was reduced by 51.54% for the FC and 56.38% for the PWP by the ANN model, compared with the linear model used in the previous literature. The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). Between the ANN and GEP models, the ANN model showed a slightly higher model of interpretability; however, the GEP model exhibited a similar or better ability to avoid large error, based on the error distribution. Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling.
Full article
(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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Graphical abstract
Open AccessArticle
Observed Energy Use by Broiler and Pullet Farms
by
Aaron P. Turner, John P. Chastain and Hunter F. Massey
AgriEngineering 2024, 6(3), 2577-2591; https://doi.org/10.3390/agriengineering6030150 - 1 Aug 2024
Abstract
Evaluating farm-scale energy used for broiler production can provide insight into how these facilities use energy and allow for seasonal and managerial influences to be evaluated. This study evaluated farm-scale energy consumption for South Carolina broiler production using energy records from 17 broiler
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Evaluating farm-scale energy used for broiler production can provide insight into how these facilities use energy and allow for seasonal and managerial influences to be evaluated. This study evaluated farm-scale energy consumption for South Carolina broiler production using energy records from 17 broiler and 4 pullet farms. Monthly electric use showed low to moderate correlation (r ranging from 0.476–0.630) with ambient temperature but had limited predictive usefulness. There was no clear pattern in monthly electrical energy use for broiler barns. However, pullet barns were more consistent and could be grouped into seasons. No significant differences (p < 0.05) in annual electric or gas use were observed between farms with generally better equipment and otherwise similar farms, but production type did influence annual electric use. The average annual electrical use was 23.6 kWh m−2 for farms producing larger birds, 8.7 kWh m−2 for those producing smaller birds, and 17.0 kWh m−2 for pullet farms. Electrical energy use accounted for 37% of total energy in broiler barns and 32% of energy in pullet barns. Combined electric and gas consumption averaged 214 MJ m−2 yr−1 across all farms. These findings help better quantify farm-scale energy used for broiler production and provide benchmark values for energy use.
Full article
(This article belongs to the Section Livestock Farming Technology)
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