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: 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 5.6 days (median values for papers published in this journal in the second half of 2023).
- 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:
2.8 (2022);
5-Year Impact Factor:
2.7 (2022)
Latest Articles
Bioactive Compound Extraction of Hemp (Cannabis sativa L.) Leaves through Response Surface Methodology Optimization
AgriEngineering 2024, 6(2), 1300-1318; https://doi.org/10.3390/agriengineering6020075 (registering DOI) - 9 May 2024
Abstract
Hemp, commonly known as Cannabis sativa L., is a medicinal plant species of the Cannabaceae family. For the efficient extraction of C. sativa leaves using the conventional stirring process with water as the solvent, three crucial extraction parameters (i.e., extraction duration, liquid–solid ratio,
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Hemp, commonly known as Cannabis sativa L., is a medicinal plant species of the Cannabaceae family. For the efficient extraction of C. sativa leaves using the conventional stirring process with water as the solvent, three crucial extraction parameters (i.e., extraction duration, liquid–solid ratio, and temperature) were investigated through the response surface methodology (RSM). The concentrations of the extracted bioactive compounds (polyphenols, ascorbic acid, and carotenoids) showed significant variations in the RSM design points, suggesting the importance of finding the optimal extraction conditions in which liquid–solid ratio and extraction temperature were found to have the highest impact. Further analysis was conducted on the optimal extract employing several assays to determine their polyphenol content, total carotenoid content, color evaluation, anti-inflammatory activity, and antioxidant capacity through FRAP, DPPH, and H2O2 assays. Α low extraction time (30 min) at 50 °C and a high liquid–solid ratio (50:1) were required for the highest possible yield of polyphenols. The total polyphenol content was determined to be 9.76 mg gallic acid equivalents/g under optimum conditions, with pelargonin being the most abundant polyphenol (1.51 mg/g) in C. sativa extracts. Ascorbic acid was measured at 282.23 μg/g and total carotenoids at 356.98 μg/g. Correlation analyses revealed that anti-inflammatory activity was negatively correlated with specific polyphenols. As determined by DPPH (27.43 μmol ascorbic acid equivalents (AAE)/g), FRAP (49.79 μmol AAE/g), and H2O2 (230.95 μmol AAE/g) assays, the optimized aqueous extract showed a high antioxidant capacity. Furthermore, it demonstrated considerable anti-inflammatory activity at 17.89%, with the potential to increase to 75.12% under particular extraction conditions. Given the high added-value of the aqueous extracts, the results of this study highlight the potential utility of C. sativa leaves as a source of health-improving antioxidant compounds in the pharmaceutical and food industries.
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Open AccessArticle
The Cultivation of Spirulina maxima in a Medium Supplemented with Leachate for the Production of Biocompounds: Phycocyanin, Carbohydrates, and Biochar
by
Wallyson Ribeiro dos Santos, Matheus Lopes da Silva, Geronimo Virginio Tagliaferro, Ana Lucia Gabas Ferreira and Daniela Helena Pelegrine Guimarães
AgriEngineering 2024, 6(2), 1289-1299; https://doi.org/10.3390/agriengineering6020074 (registering DOI) - 9 May 2024
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Cyanobacteria are microorganisms that grow rapidly in an aquatic medium, showing the capacity of accumulations of biocompounds subsequently converted into value-added biocompounds. The cyanobacterium Spirulina maxima can produce pigments besides accumulating significant amounts of carbohydrates and proteins. An alternative to reducing biomass production
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Cyanobacteria are microorganisms that grow rapidly in an aquatic medium, showing the capacity of accumulations of biocompounds subsequently converted into value-added biocompounds. The cyanobacterium Spirulina maxima can produce pigments besides accumulating significant amounts of carbohydrates and proteins. An alternative to reducing biomass production costs at an industrial scale is the use of landfill leachate in the growing medium, as well as the mitigation of this pollutant. The objective of this work was to cultivate Spirulina maxima in a medium supplemented with leachate, using the design of experiments to evaluate the effects of leachate concentration (% v/v), light source, and light intensity in an airlift photobioreactor, analyzing them as a response to the productivity of biomass, phycocyanin, carbohydrates, and biochar. The highest values of productivity (mg L−1d−1) were 97.44 ± 3.20, 12.82 ± 0.38, 6.19 ± 1.54, and 34.79 ± 3.62 for biomass, carbohydrates, phycocyanin, and biochar, respectively, adjusted for experiment 2 with the factors of leachate concentration (5.0% v/v), light source (tubular LED), and luminosity (54 µmol m−2 s−1), respectively. The use of leachate as a substitute for macronutrients in Zarrouk’s medium for the cultivation of Spirulina maxima is a viable alternative in the production of biocompounds as long as it is used at an appropriate level.
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Open AccessArticle
The Effect of Furrow Opener and Disc Coulter Configurations on Seeding Performance under Different Residue Cover Densities
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Davut Karayel, Eglė Jotautienė and Egidijus Šarauskis
AgriEngineering 2024, 6(2), 1277-1288; https://doi.org/10.3390/agriengineering6020073 (registering DOI) - 9 May 2024
Abstract
The performance of the no-till seeder is one of the most important factors that affect the success of the no-tillage. Striking the right balance between furrow opener design and residue cover is essential for optimizing seeding conditions and ensuring sustainable agricultural practices that
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The performance of the no-till seeder is one of the most important factors that affect the success of the no-tillage. Striking the right balance between furrow opener design and residue cover is essential for optimizing seeding conditions and ensuring sustainable agricultural practices that promote both soil conservation and high-yield crop production. This study investigates the impact of residue cover on no-tillage maize seeding after wheat harvest, focusing on plant spacing, seeding depth, mean emergence time, and percent emergence. Trials with hoe-type and double-disc-type furrow openers, accompanied by plain- or ripple-disc-type coulters, were conducted in Antalya, Turkey. The results indicate that residue cover had no significant effect on mean plant spacing, but a higher residue cover increased spacing variation. The seeding depth in hoe-type furrow opener trials remained consistent, while double-disc-type furrow openers showed lower depths with 80% and 90% residue covers. The percentage of plant emergence and mean emergence time decreased as the residue cover increased in double-disc-type furrow opener trials. At 90% residue cover, PE decreased to 60%. The impact of disc coulters on hoe-type furrow openers was limited, but they increased seeding depth and MET in double-disc-type furrow openers. These findings can help optimize residue management for improved efficiency in no-till farming systems.
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(This article belongs to the Section Agricultural Mechanization and Machinery)
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Nighttime Harvesting of OrBot (Orchard RoBot)
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Jakob Waltman, Ethan Buchanan and Duke M. Bulanon
AgriEngineering 2024, 6(2), 1266-1276; https://doi.org/10.3390/agriengineering6020072 - 8 May 2024
Abstract
The Robotics Vision Lab of Northwest Nazarene University has developed the Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and
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The Robotics Vision Lab of Northwest Nazarene University has developed the Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and a gripper to remove the target fruit. Field trials conducted at commercial orchards for apples and peaches during the harvesting season of 2021 yielded a harvesting success rate of about 85% and had an average harvesting cycle time of 12 s. Building upon this success, the goal of this study is to evaluate the performance of OrBot during nighttime harvesting. The idea is to have OrBot harvest at night, and then human pickers continue the harvesting operation during the day. This human and robot collaboration will leverage the labor shortage issue with a relatively slower robot working at night. The specific objectives are to determine the artificial lighting parameters suitable for nighttime harvesting and to evaluate the harvesting viability of OrBot during the night. LED lighting was selected as the source for artificial illumination with a color temperature of 5600 K and 10% intensity. This combination resulted in images with the lowest noise. OrBot was tested in a commercial orchard using twenty Pink Lady apple trees. Results showed an increased success rate during the night, with OrBot gaining 94% compared to 88% during the daytime operations.
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(This article belongs to the Section Sensors Technology and Precision Agriculture)
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Open AccessArticle
Applying Paraconsistent Annotated Logic Eτ for Optimizing Broiler Housing Conditions
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Angel Antonio Gonzalez Martinez, Irenilza de Alencar Nääs, Thayla Morandi Ridolfi de Carvalho-Curi and Jair Minoro Abe
AgriEngineering 2024, 6(2), 1252-1265; https://doi.org/10.3390/agriengineering6020071 - 6 May 2024
Abstract
Broilers are particularly sensitive to heat stress, which can impair growth, and lower conversion efficiency and survival rates. Under a climate change scenario, maintaining optimal thermal conditions within broiler houses becomes more complex and energy-intensive. Climate change can worsen air quality issues inside
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Broilers are particularly sensitive to heat stress, which can impair growth, and lower conversion efficiency and survival rates. Under a climate change scenario, maintaining optimal thermal conditions within broiler houses becomes more complex and energy-intensive. Climate change can worsen air quality issues inside broiler houses by increasing the concentration of harmful gases, and proper mechanical ventilation systems are essential for diluting and removing these gases. The present study aimed to develop and validate a model for the ideal broiler housing strategy by applying the Paraconsistent Annotated Evidential Logic Eτ. A database from four broiler houses in a commercial farm, rearing 157,700 birds from the 1st to the 42nd day of growth, was used in the research. All environmental data were recorded weekly inside the houses, and on day 42, flock mortality, overall feed-to-gain ratio, and body weight were calculated and registered. The Cohen’s Kappa statistics for each environmental parameter classification compared to the paraconsistent classification. Results indicated that temperature shows good agreement, relative humidity shows slight agreement, air velocity presents a good agreement, CO2 concentration has a slight agreement, and NH3 concentration is classified by slight agreement. The environmental and productivity variables as a function of the broiler age using the extreme True paraconsistent state indicate the model validation. The paraconsistent analysis presented the ideal scenario for broilers’ growth, maintaining the environmental variables level within a particular threshold and providing greater profit to broiler farmers.
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(This article belongs to the Section Livestock Farming Technology)
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Optimizing Convolutional Neural Networks, XGBoost, and Hybrid CNN-XGBoost for Precise Red Tilapia (Oreochromis niloticus Linn.) Weight Estimation in River Cage Culture with Aerial Imagery
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Wara Taparhudee, Roongparit Jongjaraunsuk, Sukkrit Nimitkul, Pimlapat Suwannasing and Wisit Mathurossuwan
AgriEngineering 2024, 6(2), 1235-1251; https://doi.org/10.3390/agriengineering6020070 - 2 May 2024
Abstract
Accurate feeding management in aquaculture relies on assessing the average weight of aquatic animals during their growth stages. The traditional method involves using a labor-intensive approach and may impact the well-being of fish. The current research focuses on a unique way of estimating
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Accurate feeding management in aquaculture relies on assessing the average weight of aquatic animals during their growth stages. The traditional method involves using a labor-intensive approach and may impact the well-being of fish. The current research focuses on a unique way of estimating red tilapia’s weight in cage culture via a river, which employs unmanned aerial vehicle (UAV) and deep learning techniques. The described approach includes taking pictures by means of a UAV and then applying deep learning and machine learning algorithms to them, such as convolutional neural networks (CNNs), extreme gradient boosting (XGBoost), and a Hybrid CNN-XGBoost model. The results showed that the CNN model achieved its accuracy peak after 60 epochs, showing accuracy, precision, recall, and F1 score values of 0.748 ± 0.019, 0.750 ± 0.019, 0.740 ± 0.014, and 0.740 ± 0.019, respectively. The XGBoost reached its accuracy peak with 45 n_estimators, recording values of approximately 0.560 ± 0.000 for accuracy and 0.550 ± 0.000 for precision, recall, and F1. Regarding the Hybrid CNN-XGBoost model, it demonstrated its prediction accuracy using both 45 epochs and n_estimators. The accuracy value was around 0.760 ± 0.019, precision was 0.762 ± 0.019, recall was 0.754 ± 0.019, and F1 was 0.752 ± 0.019. The Hybrid CNN-XGBoost model demonstrated the highest accuracy compared to using standalone CNN and XGBoost models and could reduce the time required for weight estimation by around 11.81% compared to using the standalone CNN. Although the testing results may be lower than those from previous laboratory studies, this discrepancy is attributed to the real-world testing conditions in aquaculture settings, which involve uncontrollable factors. To enhance accuracy, we recommend increasing the sample size of images and extending the data collection period to cover one year. This approach allows for a comprehensive understanding of the seasonal effects on evaluation outcomes.
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Open AccessArticle
Experimental Evaluation of Nano Coating on the Draft Force of Tillage Implements and Its Prediction Using an Adaptive Neuro-Fuzzy Inference System (ANFIS)
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Saeed Mehrang Marani, Gholamhossein Shahgholi, Mariusz Szymanek and Wojciech Tanaś
AgriEngineering 2024, 6(2), 1218-1234; https://doi.org/10.3390/agriengineering6020069 - 29 Apr 2024
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The effect of coating a flat blade surface with titanium nitride nano coatings (TiN), nano tantalum carbide (TaC), Fiberglass (Glass Fiber-Reinforced Polymer) (GFRP), Galvanized Steel (GAS), and St37 (SST37) was investigated in order to decrease the adhesion of soil on tilling tools, external
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The effect of coating a flat blade surface with titanium nitride nano coatings (TiN), nano tantalum carbide (TaC), Fiberglass (Glass Fiber-Reinforced Polymer) (GFRP), Galvanized Steel (GAS), and St37 (SST37) was investigated in order to decrease the adhesion of soil on tilling tools, external friction and, ultimately, the draft force. The soil tank, which was filled with soil of the desired conditions, was pulled on the bearing on the rail. A S-shaped load cell was used to measure the draft force. Tests were conducted at a distance of 2 m and speeds of 0.1, 0.2, and 0.3 m·s−1 at a depth of 10 cm. A model based on input factors, including blade travel speed, rake angle, and cohesion and adhesion of soil–blade, was developed in an adaptive neuro-fuzzy inference system (ANFIS), and draft force was the output parameter. To verify the performance of the developed model using ANFIS, a relative error(ε) of 6.1% and coefficient of determination (R2) of 0.956 were computed. It was found that blades coated with Nano (TiN-TaC), due to its hydrophobic surface, flatness, and self-cleaning properties, have considerable ability to decrease adhesion in wet soils and showed a linear relationship with draft force reduction.
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Open AccessReview
An Overview of the Mechanisms of Action and Administration Technologies of the Essential Oils Used as Green Insecticides
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Irinel Eugen Popescu, Irina Neta Gostin and Cristian Felix Blidar
AgriEngineering 2024, 6(2), 1195-1217; https://doi.org/10.3390/agriengineering6020068 - 26 Apr 2024
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The need to use environmentally friendly substances in agriculture for pest control has become increasingly urgent in recent years. This was generated by humanity’s awareness of the harmful effects of chemicals with increased persistence, which accumulated in nature and harmed living beings. Essential
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The need to use environmentally friendly substances in agriculture for pest control has become increasingly urgent in recent years. This was generated by humanity’s awareness of the harmful effects of chemicals with increased persistence, which accumulated in nature and harmed living beings. Essential oils are among the most important biopesticides and could significantly contribute to the expansion of ecological agriculture, replacing traditional methods. However, for judicious use, it is necessary to have a thorough knowledge of the mechanisms by which these oils act on both harmful and useful insects. An important step in transitioning from theory to practice is adapting essential oil application technologies for open fields, overcoming the difficulties created by their high volatility and low remanence, which results in a rapid reduction in the toxic effect. The review proposes an in-depth, up-to-date analysis of the existing literature on these subjects, aiming to provide researchers with some potential future study directions and practitioners with a solid base of information regarding the interaction between insects and essential oils.
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Agricultural Practices for Biodiversity Enhancement: Evidence and Recommendations for the Viticultural Sector
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Sara M. Marcelino, Pedro Dinis Gaspar, Arminda do Paço, Tânia M. Lima, Ana Monteiro, José Carlos Franco, Erika S. Santos, Rebeca Campos and Carlos M. Lopes
AgriEngineering 2024, 6(2), 1175-1194; https://doi.org/10.3390/agriengineering6020067 - 26 Apr 2024
Abstract
Agricultural expansion and intensification worldwide has caused a reduction in ecological infrastructures for insects, herbaceous plants, and vertebrate insectivores, among other organisms. Agriculture is recognized as one of the key influences in biodiversity decline, and initiatives such as the European Green Deal highlight
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Agricultural expansion and intensification worldwide has caused a reduction in ecological infrastructures for insects, herbaceous plants, and vertebrate insectivores, among other organisms. Agriculture is recognized as one of the key influences in biodiversity decline, and initiatives such as the European Green Deal highlight the need to reduce ecosystem degradation. Among fruit crops, grapes are considered one of the most intensive agricultural systems with the greatest economic relevance. This study presents a compilation of management practices to enhance biodiversity performance, which applies generally to the agricultural sector and, in particular, to viticulture, concerning the diversity of plants, semi-natural habitats, soil management, and the chemical control strategies and pesticides used in agricultural cultivation. Through a critical review, this study identifies a set of recommendations for biodiversity performance and their corresponding effects, contributing to the dissemination of management options to boost biodiversity performance. The results highlight opportunities for future investigations in determining the needed conditions to ensure both biodiversity enhancement and productive gains, and understanding the long-term effects of innovative biodiversity-friendly approaches.
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Open AccessArticle
RisDes_Index: An Index for Analysing the Advance of Areas Undergoing Desertification Using Satellite Data
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Thieres George Freire da Silva, José Francisco da Cruz Neto, Alexandre Maniçoba da Rosa Ferraz Jardim, Carlos André Alves de Souza, George do Nascimento Araújo Júnior, Marcos Vinícius da Silva, Jhon Lennon Bezerra da Silva, Ailton Alves de Carvalho, Abelardo Antônio de Assunção Montenegro and Luciana Sandra Bastos de Souza
AgriEngineering 2024, 6(2), 1150-1174; https://doi.org/10.3390/agriengineering6020066 - 26 Apr 2024
Abstract
The proposal for a method of identifying the occurrence of desertification that has a strong association with in situ data leads to more assertive results when analysing the contribution of climate and social and economic factors to advancing the process. This study aimed
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The proposal for a method of identifying the occurrence of desertification that has a strong association with in situ data leads to more assertive results when analysing the contribution of climate and social and economic factors to advancing the process. This study aimed to develop a methodology called the RisDes_Index to evaluate the evolution of the desertification process based on satellite data. The concept of the RisDes_Index method was based on the reflectance variables of the R, B and G bands, albedo and LAI of the Landsat 5/TM and Landsat 8/OLI satellites. Principal component analysis was used to assess the biophysical basis of the RisDes_Index by associating the results with micrometeorological data, physical and chemical properties, and vegetation cover data collected from five experimental sites in the semi-arid region of Brazil. These sites included one from a seasonally dry forest (i.e., the Caatinga), an agricultural cactus plantation, an area undergoing desertification, and two irrigated sugarcane crops (wetlands), one with and one without straw cover. The RisDes_Index was applied to all pixels of the images from 5 December 1991, 14 November 2001, 20 November 2009 and 6 October 2016 of an important desertification nucleus (DN) in the semi-arid region of Brazil, i.e., the DN of Cabrobó. The proposed RisDes_Index was able to identify areas with significant processes of desertification, which mainly occur in areas of sandy, acidic, bare soils with a high β value (Bowen ratio) and high soil temperature. The results of the RisDes_Index showed that in 5 December 1991, desertified areas comprised 38% of the total area of the DN of Cabrobó, expanding to 51% in 2016. Application of the RisDes_Index confirmed the advance of desertification in the DN of Cabrobó. This was due to a consequent increase in the water deficit and intensified deforestation to increase the areas of livestock farming. The RisDes_Index proved to be a robust method, as its estimation based on simple satellite products exhibited a strong association with biophysical variables of areas with different land uses and degradation levels. Thus, it is suggested that the RisDes_Index be applied in various regions of the world, with the idea of directing action to meet the advance of desertification.
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(This article belongs to the Section Remote Sensing in Agriculture)
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Open AccessArticle
Utilizing Deep Neural Networks for Chrysanthemum Leaf and Flower Feature Recognition
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Toan Khac Nguyen, Minh Dang, Tham Thi Mong Doan and Jin Hee Lim
AgriEngineering 2024, 6(2), 1133-1149; https://doi.org/10.3390/agriengineering6020065 - 25 Apr 2024
Abstract
Chrysanthemums, a significant genus within the Asteraceae, hold a paramount position in the global floricultural industry, second only to roses in market demand. The proliferation of diverse chrysanthemum cultivars presents a formidable challenge for accurate identification, exacerbated by the abundance of varieties, intricate
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Chrysanthemums, a significant genus within the Asteraceae, hold a paramount position in the global floricultural industry, second only to roses in market demand. The proliferation of diverse chrysanthemum cultivars presents a formidable challenge for accurate identification, exacerbated by the abundance of varieties, intricate floral structures, diverse floret types, and complex genetic profiles. Precise recognition of chrysanthemum phenotypes is indispensable to navigating these complexities. Traditional methods, including morphology studies, statistical analyses, and molecular markers, have fallen short due to their manual nature and time-intensive processes. This study presents an innovative solution employing deep learning techniques for image-based chrysanthemum phenotype recognition. Leveraging machine learning, our system autonomously extracts key features from chrysanthemum images, converting morphological data into accessible two-dimensional representations. We utilized Support Vector Machine (SVM) and Multilayer Perceptron (MLP) algorithms to construct frameworks for processing image data and classifying chrysanthemum cultivars based on color, shape, and texture. Experimental results, encompassing 10 cultivars, 10 flower colors, and five flower shapes, consistently demonstrated recognition accuracy ranging from 79.29% up to 97.86%. This tool promises streamlined identification of flower traits, and we anticipate its potential for real-time identification enhancements in future iterations, promising advances in chrysanthemum cultivation and exportation processes. Our approach offers a novel and efficient means to address the challenges posed by the vast diversity within chrysanthemum species, facilitating improved management, breeding, and marketing strategies in the floricultural industry.
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(This article belongs to the Section Computer Applications and Artificial Intelligence in Agriculture)
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Sugarcane Water Productivity for Bioethanol, Sugar and Biomass under Deficit Irrigation
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Fernando da Silva Barbosa, Rubens Duarte Coelho, Timóteo Herculino da Silva Barros, Jonathan Vásquez Lizcano, Eusímio Felisbino Fraga Júnior, Lucas da Costa Santos, Daniel Philipe Veloso Leal, Nathália Lopes Ribeiro and Jéfferson de Oliveira Costa
AgriEngineering 2024, 6(2), 1117-1132; https://doi.org/10.3390/agriengineering6020064 - 23 Apr 2024
Abstract
Knowledge of how certain crops respond to water stress is one of the prerequisites for choosing the best variety and best management practices to maximize crop water productivity (WPc). The selection of a more efficient protocol for managing irrigation depths throughout
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Knowledge of how certain crops respond to water stress is one of the prerequisites for choosing the best variety and best management practices to maximize crop water productivity (WPc). The selection of a more efficient protocol for managing irrigation depths throughout the cultivation cycle and in the maturation process at the end of the growth period for each sugarcane variety can maximize bioethanol productivity and WPc for bioethanol, sugar and biomass, in addition to the total energy captured by the sugarcane canopy in the form of dry biomass. This study aimed to evaluate the effect of four irrigation depths and four water deficit intensities on the maturation phase for eight sugarcane varieties under drip irrigation, analyzing the responses related to WPc for bioethanol, sugar and biomass. These experiments were conducted at the University of São Paulo. The plots were positioned in three randomized blocks, and the treatments were distributed in a factorial scheme (4 × 8 × 4). The treatments involved eight commercial varieties of sugarcane and included four water replacement levels and four water deficits of increasing intensity in the final phase of the crop season. It was found that for each variety of sugarcane, there was an optimal combination of irrigation management strategies throughout the cycle and during the maturation process. The RB966928 variety resulted in the best industrial bioethanol yield (68.7 L·Mg−1), WPc for bioethanol (0.97 L·m−3) and WPc for sugar (1.71 kg·m−3). The energy of the aerial parts partitioned as sugar had a direct positive correlation with the availability of water in the soil for all varieties. The RB931011 variety showed the greatest potential for converting water into shoots with an energy of 1.58 GJ·ha−1·mm−1, while the NCo376 variety had the lowest potential at 1.32 GJ·ha−1·mm−1. The productivity of first-generation bioethanol had the highest values per unit of planted area for the greatest water volumes applied and transpired by each variety; this justifies keeping soil moisture at field capacity until harvesting time only for WR100 water replacement level with a maximum ethanol potential of 13.27 m3·ha−1.
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(This article belongs to the Special Issue Sustainable Development of Agroecosystems: Advances in Agricultural Engineering)
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Open AccessArticle
Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning
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Lwandile Nduku, Cilence Munghemezulu, Zinhle Mashaba-Munghemezulu, Phathutshedzo Eugene Ratshiedana, Sipho Sibanda and Johannes George Chirima
AgriEngineering 2024, 6(2), 1093-1116; https://doi.org/10.3390/agriengineering6020063 - 22 Apr 2024
Abstract
Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However,
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Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R2 = 0.56, RMSE = 21.01 cm) and SVM (R2 = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R2 = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R2 = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p < 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops.
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(This article belongs to the Special Issue Application of Remote Sensing and GIS in Agricultural Engineering)
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High-Throughput Phenotyping: Application in Maize Breeding
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Ewerton Lélys Resende, Adriano Teodoro Bruzi, Everton da Silva Cardoso, Vinícius Quintão Carneiro, Vitório Antônio Pereira de Souza, Paulo Henrique Frois Correa Barros and Raphael Rodrigues Pereira
AgriEngineering 2024, 6(2), 1078-1092; https://doi.org/10.3390/agriengineering6020062 - 20 Apr 2024
Abstract
In breeding programs, the demand for high-throughput phenotyping is substantial as it serves as a crucial tool for enhancing technological sophistication and efficiency. This advanced approach to phenotyping enables the rapid and precise measurement of complex traits. Therefore, the objective of this study
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In breeding programs, the demand for high-throughput phenotyping is substantial as it serves as a crucial tool for enhancing technological sophistication and efficiency. This advanced approach to phenotyping enables the rapid and precise measurement of complex traits. Therefore, the objective of this study was to estimate the correlation between vegetation indices (VIs) and grain yield and to identify the optimal timing for accurately estimating yield. Furthermore, this study aims to employ photographic quantification to measure the characteristics of corn ears and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in a Complete Randomized Block (CRB) design with three replications across three locations. Vegetation and green leaf area indices were estimated throughout the growing cycle using an unmanned aerial vehicle (UAV) and were subsequently correlated with grain yield. The experiments consistently exhibited high levels of experimental quality across different locations, characterized by both high accuracy and low coefficients of variation. The experimental quality was consistently significant across all sites, with accuracy ranging from 79.07% to 95.94%. UAV flights conducted at the beginning of the crop cycle revealed a positive correlation between grain yield and the evaluated vegetation indices. However, a positive correlation with yield was observed at the V5 vegetative growth stage in Lavras and Ijaci, as well as at the V8 stage in Nazareno. In terms of corn ear phenotyping, the regression coefficients for ear width, length, and total number of grains (TNG) were 0.92, 0.88, and 0.62, respectively, demonstrating a strong association with manual measurements. The use of imaging for ear phenotyping is promising as a method for measuring corn components. It also enables the identification of the optimal timing to accurately estimate corn grain yield, leading to advancements in the agricultural imaging sector by streamlining the process of estimating corn production.
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(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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Modern Floating Greenhouses: Planting Gray Oyster Mushrooms with Advanced Management Technology Including Mobile Phone Algorithms and Arduino Remote Control
by
Grianggai Samseemoung, Phongsuk Ampha, Niti Witthayawiroj, Supakit Sayasoonthorn and Theerapat Juey
AgriEngineering 2024, 6(2), 1055-1077; https://doi.org/10.3390/agriengineering6020061 - 19 Apr 2024
Abstract
A floating greenhouse for growing oyster mushrooms can be operated remotely via a mobile phone. This innovative system can enhance mushroom production and quality while saving time. By using the Android OS operating system on a mobile phone (Internet Mobile Device with Android
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A floating greenhouse for growing oyster mushrooms can be operated remotely via a mobile phone. This innovative system can enhance mushroom production and quality while saving time. By using the Android OS operating system on a mobile phone (Internet Mobile Device with Android OS, MGT Model: T10), users can adjust the humidity and temperature within the greenhouse. This approach is particularly beneficial for older adults. Create a smart floating greenhouse that can be controlled remotely to cultivate oyster mushrooms. It would help to enhance the quality of the mushrooms, reduce the time required for cultivation, and increase the yield per planting area. We carefully examined the specifications and proceeded to create a greenhouse that could float. In addition, we have developed a unit that could control temperature and humidity, a solar cell unit, and a rack for growing mushrooms. Our greenhouses were operated remotely. To determine the best conditions for growing plants in a floating greenhouse, we conducted a test to measure temperature and humidity. We then compared our findings to those of a traditional greenhouse test and determined the optimal parameters for floating greenhouse growth. These parameters include growth time, temperature, humidity, and weight. A mushroom nursery that can be controlled remotely and floats on water consists of four main components: a structure to regulate temperature and humidity, solar cells, and mushroom racks. Research shows that mushrooms grown under this automated control system grow better than those grown through traditional methods. The harvest period is shorter, and the yield is higher than the typical yield of 1.81–1.22. When considering the construction and use of remote-controlled floating mushroom nurseries, the daily weight of mushrooms accounted for 20.22%. The company’s investment return rates were found to be 3.47 years, or 580.21 h per year, which is higher than the yield of traditional methods. This mobile phone remote control system, created by Arduino, is tailor-made for cutting-edge floating greenhouses that grow grey oyster mushrooms. It can be operated with ease via mobile devices and is especially user-friendly for elderly individuals. This system enables farmers to produce a high volume of quality breeds. Furthermore, those with fish ponds can utilize the system to increase their profits.
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(This article belongs to the Section Sensors Technology and Precision Agriculture)
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Open AccessArticle
Preliminary Study on the Effect of Artificial Lighting on the Production of Basil, Mustard, and Red Cabbage Seedlings
by
Bruna Maran, Wendel Paulo Silvestre and Gabriel Fernandes Pauletti
AgriEngineering 2024, 6(2), 1043-1054; https://doi.org/10.3390/agriengineering6020060 - 16 Apr 2024
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The use of artificial lighting in a total or supplementary way is a current trend, with growing interest due to the increase in the global population and climate change, which require high-yield, quality, and fast-growing crops with less water and a smaller carbon
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The use of artificial lighting in a total or supplementary way is a current trend, with growing interest due to the increase in the global population and climate change, which require high-yield, quality, and fast-growing crops with less water and a smaller carbon footprint. This experiment aimed to evaluate the effect of light-emitting diode (LED) lighting on the production of basil, mustard, and red cabbage seedlings under controlled artificial conditions and in a greenhouse as a supplementary lighting regime. Under controlled conditions, the experiment was conducted with basil seedlings, comparing LED light with two wavelengths (purple and white light). In a greenhouse, mustard and red cabbage seedlings were evaluated under natural light (regular photoperiod) and with supplementary purple lighting of 3 h added to the photoperiod. The variables assessed were aerial fresh mass (AFM), aerial dry mass (ADM), root dry mass (RDM), plant length (PL), and leaf area (LA). Basil seedlings grown under purple light showed greater length and AFM than those grown under white light, with no effect on the production of secondary metabolites. In the greenhouse experiment, red cabbage seedlings showed an increase in AFM, ADM, and DRM with light supplementation, with no effect on LA. AFM showed no statistical difference in mustard seedlings, but the productive parameters LA, ADM, and DRM were higher with supplementation. None of the evaluated treatments influenced the production of phenolic compounds and flavonoids in the three species evaluated. Light supplementation affected red cabbage and mustard seedlings differently, promoting better development in some production parameters without affecting the production of phenolic compounds and flavonoids in either plant. Thus, light supplementation or artificial lighting can be considered a tool to enhance and accelerate the growth of seedlings, increasing productivity and maintaining the quality of the secondary metabolites evaluated. Thus, this technology can reduce operational costs, enable cultivation in periods of low natural light and photoperiod, and cultivate tropical species in temperate environments in completely artificial (indoor) conditions.
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Natural Compounds and Derivates: Alternative Treatments to Reduce Post-Harvest Losses in Fruits
by
Edson Rayón-Díaz, Luis G. Hernández-Montiel, Jorge A. Sánchez-Burgos, Victor M. Zamora-Gasga, Ramsés Ramón González-Estrada and Porfirio Gutiérrez-Martínez
AgriEngineering 2024, 6(2), 1022-1042; https://doi.org/10.3390/agriengineering6020059 - 16 Apr 2024
Abstract
The effects of phytopathogenic fungi on fruits and vegetables are a significant global concern, impacting various sectors including social, economic, environmental, and consumer health. This issue results in diminished product quality, affecting a high percentage of globally important fruits. Over the last 20
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The effects of phytopathogenic fungi on fruits and vegetables are a significant global concern, impacting various sectors including social, economic, environmental, and consumer health. This issue results in diminished product quality, affecting a high percentage of globally important fruits. Over the last 20 years, the use of chemical products in the agri-food sector has increased by 30%, leading to environmental problems such as harm to main pollinators, high levels of chemical residue levels, development of resistance in various phytopathogens, and health issues. As a response, various organizations worldwide have proposed programs aimed at reducing the concentration of active compounds in these products. Priority is given to alternative treatments that can mitigate environmental impact, control phytopathogens, and ensure low residuality and toxicity in fruits and vegetables. This review article presents the mechanisms of action of three alternative treatments: chitosan, citral, and hexanal. These treatments have the potential to affect the development of various pathogenic fungi found in tropical and subtropical fruits. It is important to note that further studies to verify the effects of these treatments, particularly when used in combination, are needed. Integrating the mechanisms of action of each treatment and exploring the possibility of generating a broad-spectrum effect on the development of pathogenic microorganisms in fruits is essential for a comprehensive understanding and effective management.
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(This article belongs to the Special Issue Novel Methods for Food Product Preservation)
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Combining Image Classification and Unmanned Aerial Vehicles to Estimate the State of Explorer Roses
by
David Herrera, Pedro Escudero-Villa, Eduardo Cárdenas, Marcelo Ortiz and José Varela-Aldás
AgriEngineering 2024, 6(2), 1008-1021; https://doi.org/10.3390/agriengineering6020058 - 16 Apr 2024
Abstract
The production of Explorer roses has historically been attractive due to the acceptance of the product around the world. This species of roses presents high sensitivity to physical contact and manipulation, creating a challenge to keep the final product quality after cultivation. In
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The production of Explorer roses has historically been attractive due to the acceptance of the product around the world. This species of roses presents high sensitivity to physical contact and manipulation, creating a challenge to keep the final product quality after cultivation. In this work, we present a system that combines the capabilities of intelligent computer vision and unmanned aerial vehicles (UAVs) to identify the state of roses ready for cultivation. The system uses a deep learning-based approach to estimate Explorer rose crop yields by identifying open and closed rosebuds in the field using videos captured by UAVs. The methodology employs YOLO version 5, along with DeepSORT algorithms and a Kalman filter, to enhance counting precision. The evaluation of the system gave a mean average precision (mAP) of 94.1% on the test dataset, and the rosebud counting results obtained through this technique exhibited a strong correlation (R2 = 0.998) with manual counting. This high accuracy allows one to minimize the manipulation and times used for the tracking and cultivation process.
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(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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Prediction of Noise Levels According to Some Exploitation Parameters of an Agricultural Tractor: A Machine Learning Approach
by
Željko Barač, Dorijan Radočaj, Ivan Plaščak, Mladen Jurišić and Monika Marković
AgriEngineering 2024, 6(2), 995-1007; https://doi.org/10.3390/agriengineering6020057 - 15 Apr 2024
Abstract
The paper presents research on measuring and the possibility of prediction of noise levels on the left and right sides of the operator within the cabin of an agricultural tractor when moving across various agrotechnical surfaces, considering movement velocity and tire pressures while
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The paper presents research on measuring and the possibility of prediction of noise levels on the left and right sides of the operator within the cabin of an agricultural tractor when moving across various agrotechnical surfaces, considering movement velocity and tire pressures while employing machine learning techniques. Noise level measurements were conducted on a LANDINI POWERFARM 100 type tractor, and aligned with standards (HRN ISO 5008, HRN ISO 6396 and HRN ISO 5131). The obtained noise values were divided into two data sets (left and right set) and processed using multiple linear regression (mlr) and three machine learning methods (gradient boosting machine (gbm); support vector machine using radial basis function kernel (svmRadial); monotone multi-layer perceptron neural network (monmlp)). The most accurate method, considering surfaces, from the left side data set—(R2 0.515–0.955); (RMSE 0.302–0.704); (MAE 0.225–0.488)—and the right side—(R2 0.555–0.955); (RMSE 0.180–0.969); (MAE 0.139–0.644)—was monmlp predominantly, and to a lesser extent svmRadial. On analyzing the total data sets from the left and right sides regarding surfaces, gbm emerged as the most accurate method. The application of machine learning methods demonstrated data accuracy, yet in future research, measurements on certain surfaces may need to be repeated multiple times potentially to improve accuracy further.
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(This article belongs to the Special Issue Implementation of Artificial Intelligence in Agriculture)
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Comparative Measurement of Horizontal Penetrometry with a Focus on the Degree of Soil Compaction in Real Work Conditions
by
Marek Mojžiš, Ján Kosiba and Ján Jobbágy
AgriEngineering 2024, 6(2), 979-994; https://doi.org/10.3390/agriengineering6020056 - 11 Apr 2024
Abstract
Potential soil production is closely related to the physical and mechanical properties. The aim of this paper was to evaluate the effect of different levels of soil compaction created by tractor chassis. The total area of the experimental plot was 13.22 ha. Up
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Potential soil production is closely related to the physical and mechanical properties. The aim of this paper was to evaluate the effect of different levels of soil compaction created by tractor chassis. The total area of the experimental plot was 13.22 ha. Up until 2019, a conventional tillage system had been used. The measurements were carried out with an innovative measuring device that allows for the continuous measurement of the horizontal penetrometry for comparative measurements while driving, which was designed at the Slovak University of Agriculture in Nitra. The measuring device measured the soil resistance in the tire track (On-track) and out of track (Off-track) as well as in three (50 s) sequences within one tractor pass. Three lines were chosen, where in each a pair of combinations was made. The results were subjected, in addition to graphical evaluation, to single factor ANOVA analysis. When comparing individual passes (PH1 to PH6), the statistical analysis showed that the results of the horizontal resistance measurements proved to be statistically significant (p < 0.05) with respect to the weight, number of passes, and tire underinflation. The highest compaction was caused by the first pass, while the higher weight of the tractor during the next pass had a smaller effect. Underinflating the tires ensured a reduction in compaction. Reducing the tractor tire pressure to 0.15 MPa resulted in a reduction in soil compaction of up to 16%.
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(This article belongs to the Section Agricultural Mechanization and Machinery)
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