AgriEngineering

21 pages, 4198 KiB

Open AccessArticle

Integrating a Centrifugal Extraction and Pipeline Transportation System to Improve Efficiency in Shrimp Harvesting Management

by Songphon Thoetrattanakiat, Kiattisak Sangpradit and Grianggai Samseemoung

AgriEngineering 2025, 7(5), 128; https://doi.org/10.3390/agriengineering7050128 - 22 Apr 2025

Abstract

The integration of a centrifugal extraction and pipeline transportation system to improve efficiency in shrimp harvesting management into closed-pond aquaculture systems represents a significant advancement in aquaculture technology. This study introduces and assesses the efficiency of the shrimp harvester compared to manual harvesting [...] Read more.

The integration of a centrifugal extraction and pipeline transportation system to improve efficiency in shrimp harvesting management into closed-pond aquaculture systems represents a significant advancement in aquaculture technology. This study introduces and assesses the efficiency of the shrimp harvester compared to manual harvesting methods, examining key parameters such as shrimp harvester quality, shrimp harvester loss rates, and shrimp harvester speed. A particularly noteworthy aspect is the innovative transportation of shrimp through a pipeline, which enhances the transformative potential of this technology. Results indicate that the centrifugal shrimp harvester outperforms manual methods, achieving an impressive yield rate of 3338 kg/h with a minimal loss rate of 0.01%; the trend values for harvester capacity ranged from 0.501 to 1.884 tons of shrimp per hour at 240 rpm, 2.391 to 3.081 tons per hour at 270 rpm, and 3.338 to 3.816 tons per hour at 300 rpm. While this technology shows promise for increasing productivity and minimizing shrimp damage, further investigation is needed to evaluate its economic viability, including operational costs and labor expenses. The study highlights the transformative potential of the centrifugal shrimp harvester and emphasizes the need for ongoing research to ensure its practical application in real-world aquaculture settings. Overall, the centrifugal shrimp harvester is poised to revolutionize shrimp harvesting practices, contributing to more sustainable and efficient aquaculture production. Full article

(This article belongs to the Special Issue Advancing Smart Farming through Agricultural Robots and Automation Technologies)

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20 pages, 6149 KiB

Open AccessArticle

Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres

by Ahmed Rady, Oliver Fisher, Aly A. A. El-Banna, Haitham H. Emasih and Nicholas J. Watson

AgriEngineering 2025, 7(5), 127; https://doi.org/10.3390/agriengineering7050127 (registering DOI) - 22 Apr 2025

Abstract

Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, cotton fibre trading in Egypt still depends on human grading of cotton quality, which is resource-intensive and faces challenges in terms [...] Read more.

Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, cotton fibre trading in Egypt still depends on human grading of cotton quality, which is resource-intensive and faces challenges in terms of subjectivity and expertise requirements. This study investigates colour vision and transfer learning to classify the grade of five long (Giza 86, Giza 90, and Giza 94) and extra-long (Giza 87 and Giza 96) staple cotton cultivars. Five Convolutional Neural networks (CNNs)—AlexNet, GoogleNet, SqueezeNet, VGG16, and VGG19—were fine-tuned, optimised, and tested on independent datasets. The highest classifications were 75.7%, 85.0%, 80.0%, 77.1%, and 90.0% for Giza 86, Giza 87, Giza 90, Giza 94, and Giza 96, respectively, with F1-Scores ranging from 51.9–100%, 66.7–100%, 42.9–100%, 40.0–100%, and 80.0–100%. Among the CNNs, AlexNet, GoogleNet, and VGG19 outperformed the others. Fused CNN models further improved classification accuracy by up to 7.2% for all cultivars except Giza 87. These results demonstrate the feasibility of developing a fast, low-cost, and low-skilled vision system that overcomes the inconsistencies and limitations of manual grading in the early stages of cotton fibre trading in Egypt. Full article

(This article belongs to the Special Issue Transforming Agriculture with Artificial Intelligence: Recent Advances and Applications)

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14 pages, 6242 KiB

Open AccessArticle

The Design and Testing of a Special Drinker for Meat Ducks Based on Reverse Engineering

by Tao Sun, Huixin Wang, Enze Duan, Gang Ma and Zongchun Bai

AgriEngineering 2025, 7(4), 126; https://doi.org/10.3390/agriengineering7040126 - 21 Apr 2025

Abstract

Background: Intensive poultry production requires highly efficient drinking systems to ensure both animal welfare and production performance; however, conventional drinkers for meat ducks often suffer from design deficiencies that compromise drinking efficiency and result in significant water wastage. Objectives: To address the drinking [...] Read more.

Background: Intensive poultry production requires highly efficient drinking systems to ensure both animal welfare and production performance; however, conventional drinkers for meat ducks often suffer from design deficiencies that compromise drinking efficiency and result in significant water wastage. Objectives: To address the drinking water demands in intensive waterfowl farming systems, a specialized drinking device tailored for meat ducks was developed. Methods: The drinking habits of meat ducks were analyzed and the performance of the existing drinkers was evaluated. The deficiencies of the current drinkers were observed and identified by high-speed video, and the parameters of the head of the meat duck were obtained by reverse-engineering technology. Based on this analysis, a specialized drinker for meat ducks was designed, and its performance was confirmed through farming trials. Results: The static and dynamic flow rate tests showed that the output of the new drinker was consistent with the nipple drinker. When the valve rod was pushed upward, the new drinker did not output, which met the design requirements. The results indicated that, under a water pressure of 2.5 kPa, the water loss rate for the designed drinker was 27.4%, which was 15.3% lower than the loss rate of 42.7% observed with the traditional nipple drinker. Conclusion: This study develops a specialized drinker for meat ducks in intensive farming, by utilizing the biting drinking method and incorporating the three-dimensional characteristics of the heads of meat ducks, significantly increasing the effective drinking rate and reducing leakage during the drinking process. Full article

(This article belongs to the Section Livestock Farming Technology)

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19 pages, 5419 KiB

Open AccessArticle

Photovoltaic Panel System with Optical Dispersion of Solar Light for Greenhouse Agricultural Applications

by Constantin Razvan Beniuga, Bogdan Andrei Pingescu, Oana Cristina Beniuga, Alin Dragomir, Dragos-George Astanei and Radu Burlica

AgriEngineering 2025, 7(4), 125; https://doi.org/10.3390/agriengineering7040125 - 18 Apr 2025

Abstract

This paper presents an innovative design of a photovoltaic panel system for agricultural applications, particularly in regions prone to drought and extreme temperatures, known as Agri-PV. The proposed solution utilizes optical elements of divergent lens types to illuminate the ground beneath photovoltaic panels [...] Read more.

This paper presents an innovative design of a photovoltaic panel system for agricultural applications, particularly in regions prone to drought and extreme temperatures, known as Agri-PV. The proposed solution utilizes optical elements of divergent lens types to illuminate the ground beneath photovoltaic panels in greenhouse or indoor controlled cultivation areas. The Agri-PV solution improves the ratio between the area occupied by the photovoltaic panels and the total cultivated area therefore the land under the photovoltaic panels is fully cultivable, produces clean electricity that can be used in the agricultural process, reduces solar energy at the ground level up to 16 times, reducing water evaporation from the ground diminishing the summer-extreme temperatures effect on crops. With an optimal vertical layout of the optical lens PV system, areas of minimum illumination can be overlapped to provide a more uniform and consistent light intensity at ground level. The overall illumination uniformity is important for maximizing energy efficiency and maintaining optimal growing conditions in agrivoltaics applications. Full article

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35 pages, 24240 KiB

Open AccessArticle

FarmSync: Ecosystem for Environmental Monitoring of Barns in Agribusiness

by Guilherme Pulizzi Costa, Geovane Yuji Aparecido Sakata, Luiz Fernando Pinto de Oliveira, Michel E. D. Chaves, Luis F. C. Duarte, Mariana Matulovic, Ricardo Fonseca Buzo and Flávio J. O. Morais

AgriEngineering 2025, 7(4), 124; https://doi.org/10.3390/agriengineering7040124 - 17 Apr 2025

Abstract

In the current era of agricultural management practices, known as agricultural 5.0, optimal indoor environments are associated with comfortable temperatures, regulated humidity, and good air quality—essential variables to improve yields. Given this scenario, there is a need for innovative ecosystems that automate indoor [...] Read more.

In the current era of agricultural management practices, known as agricultural 5.0, optimal indoor environments are associated with comfortable temperatures, regulated humidity, and good air quality—essential variables to improve yields. Given this scenario, there is a need for innovative ecosystems that automate indoor environmental monitoring in an affordable and scalable way. This paper presents the scope of the development and validation of an IoT-based ecosystem designed to monitor and control environmental conditions in agricultural barns. The objective is to present a cost-effective and easily accessible environmental monitoring system for barn buildings and agricultural storage areas, promoting the welfare of animals, humans, and crops, and contributing to the sustainable development of the agricultural industry. The system integrates wireless sensors, predictive algorithms, a web interface and cloud infrastructure to optimize temperature and humidity. A proof-of-concept assessment was performed to determine whether the modular architecture offers scalability, while the responsive web interface ensures cross-device accessibility. The results show data accuracy above 95%, prediction efficiency of 96%, and increases in production yields. This solution demonstrates economic and operational advantages over existing technologies, promoting sustainability and automation in agricultural management practices in hangars and barns, in alignment with the United Nations’ Sustainable Development Goals (SDGs). Full article

(This article belongs to the Section Computer Applications and Artificial Intelligence in Agriculture)

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15 pages, 3805 KiB

Open AccessArticle

Comparison of Volumetric Distribution in Drone Spraying Considering Height, Application Volume, and Nozzle Type

by Raí Fernandes Queiroz Alves, Jéssica Elaine Silva, Thiago Orlando Costa Barboza, Marcelo Araújo Junqueira Ferraz, Octávio Pereira da Costa, Wender Henrique Batista da Silva, Franklin Daniel Inácio, Luan Pereira de Oliveira, Christiane Augusta Diniz Melo and Adão Felipe dos Santos

AgriEngineering 2025, 7(4), 123; https://doi.org/10.3390/agriengineering7040123 - 15 Apr 2025

Abstract

The advancement of technology in agriculture has driven the use of drones for spraying, with their increasing adoption presenting challenges in calibration and volumetric distribution efficiency. This study aimed to evaluate the volumetric distribution of drone spraying by combining different operational parameters to [...] Read more.

The advancement of technology in agriculture has driven the use of drones for spraying, with their increasing adoption presenting challenges in calibration and volumetric distribution efficiency. This study aimed to evaluate the volumetric distribution of drone spraying by combining different operational parameters to determine spray swath and application uniformity. Experiments were conducted using a DJI T10 drone and a volumetric distribution table to assess the impact of different flight heights (2, 3, and 4 m), application volumes (8, 12, 16, and 20 L ha⁻¹), and nozzle types (FV 110 015, FL 110 010, and CO 080 010). Environmental conditions were monitored, and data were analyzed using histograms, analysis of variance (ANOVA) by F-test (p ≤ 0.05), and the Scott–Knott test (p ≤ 0.05) to group means. Results indicated that a lower application volume (8 L ha⁻¹) led to greater application uniformity and a narrower spray swath. Higher flight altitude (4 m) resulted in a wider spray swath and a normal distribution of spray deposition. Fine droplet nozzles (CO 080 010) enhanced uniformity, while very coarse droplets (FV 110 015) concentrated more volume in the center of the swath. Thus, using fine droplet nozzles (CO 080 010), lower application volume (8 and 12 L ha⁻¹), and higher flight altitude (4 m) as operational parameters maximizes drone spraying efficiency; however, this also increases drift potential. Full article

(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)

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17 pages, 5954 KiB

Open AccessArticle

Mapping Paddy Fields Using Satellite Images and Machine Learning to Identify High Temperature-Induced Sterility in Nankoku, Japan

by Naoyuki Hashimoto, Haruki Yamada and Shiho Matsuoka

AgriEngineering 2025, 7(4), 122; https://doi.org/10.3390/agriengineering7040122 - 15 Apr 2025

Abstract

High temperature-induced rice sterility has become a major issue in Japan; thus, the conditions influencing this sterility must be better understood to identify effective countermeasures. In this study, a random forest-based sterility estimation model was developed using the sterility rate measured via a [...] Read more.

High temperature-induced rice sterility has become a major issue in Japan; thus, the conditions influencing this sterility must be better understood to identify effective countermeasures. In this study, a random forest-based sterility estimation model was developed using the sterility rate measured via a field survey and satellite images. Applying this model to Nankoku, Japan, we attempted to map fields based on their sterility rates and visualize the spatial distribution of sterility. The results showed that the rate of change in reflectance from the heading stage until an effective accumulated temperature of 350 °C was reached was an effective model variable. Applying this model to map fields where rice sterility occurred from 2022 to 2024 revealed that more than 41% of the fields in Nankoku may have been damaged, suggesting that many fields might be at risk of adverse effects from high temperatures. The 3-year average sterility rate revealed areas with a high concentration of paddies with a low sterility rate, suggesting that investigating the environment and cultivation management techniques in these areas could provide insights to reduce the sterility rate. Moreover, the growth process up to the heading stage may contribute to the increase in the sterility rate. In the future, we plan to conduct a longitudinal survey based on the generated map to further investigate the relationships between cropping type, cultivar, and weather conditions to develop countermeasures. Full article

(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)

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17 pages, 3076 KiB

Open AccessArticle

Regression Models and Multi-Objective Optimization Using the Genetic Algorithm Technique for an Integrated Tillage Implement

by Ganesh Upadhyay, Hifjur Raheman and Rashmi Dubey

AgriEngineering 2025, 7(4), 121; https://doi.org/10.3390/agriengineering7040121 - 11 Apr 2025

Abstract

This study presents an experimental and computational analysis of the specific draft (SD) and specific torque (ST) requirements of an energy-efficient tillage implement, the active–passive disk harrow (APDH). Soil bin trials were conducted to develop multiple regression models predicting SD and ST based [...] Read more.

This study presents an experimental and computational analysis of the specific draft (SD) and specific torque (ST) requirements of an energy-efficient tillage implement, the active–passive disk harrow (APDH). Soil bin trials were conducted to develop multiple regression models predicting SD and ST based on operational parameters such as gang angle (α), speed ratio (u/v), soil cone index, and working depth. Model’s accuracy was assessed through statistical indices such as R², RMSE, MIE, and MAE. The high R² and low RMSE confirmed the reliability of the developed models in capturing the relationships between input and output variables. A genetic algorithm-based multi-objective optimization was implemented in MATLAB R2016a to determine optimal operational settings that minimize total power consumption while maximizing soil pulverization. The optimized values of α and u/v were determined to be in the ranges of 35.91° to 36.98° and 3.27 to 3.87, respectively. Model validation with laboratory and field data demonstrated acceptable prediction accuracy despite minor deviations attributed to soil variability and measurement errors. The developed models provide a predictive framework for optimizing tillage performance, aiding in tractor-implement selection, and enhancing energy efficiency in agricultural operations. Full article

(This article belongs to the Section Agricultural Mechanization and Machinery)

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16 pages, 3375 KiB

Open AccessArticle

Tomato Leaf Detection, Segmentation, and Extraction in Real-Time Environment for Accurate Disease Detection

by Shahab Ul Islam, Giampaolo Ferraioli and Vito Pascazio

AgriEngineering 2025, 7(4), 120; https://doi.org/10.3390/agriengineering7040120 - 11 Apr 2025

Abstract

Agricultural production is a critical sector that directly impacts the economy and social life of any society. The identification of plant disease in a real-time environment is a significant challenge for agriculture production. For accurate plant disease detection, precise detection of plant leaves [...] Read more.

Agricultural production is a critical sector that directly impacts the economy and social life of any society. The identification of plant disease in a real-time environment is a significant challenge for agriculture production. For accurate plant disease detection, precise detection of plant leaves is a meaningful and challenging task for developing smart agricultural systems. Most researchers train and test models on synthetic images. So, when using that model in a real-time scenario, it does not give a satisfactory result because when a model trained on images of leaves is fed with the image of the plant, then its accuracy is affected. In this research work, we have integrated two models, the Segment Anything Model (SAM) with YOLOv8, to detect the tomato leaf of a tomato plant, mask the leaf, and extract the leaf in a real-time environment. To improve the performance of leaf disease detection in plant leaves in a real-time environment, we need to detect leaves accurately. We developed a system that will detect the leaf, mask the leaf, extract the leaf, and then detect the disease in that specific leaf. For leaf detection, the modified YOLOv8 is used, and for masking and extraction of the leaf images from the tomato plant, the Segment Anything Model (SAM) is used. Then, for that specific leaf, an image is provided to the deep neural network to detect the disease. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

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18 pages, 4044 KiB

Open AccessArticle

Selective Wax Cuticle Removal Using Green Wavelength Lasers: A Non-Invasive Method for Enhancing Foliar Uptake

by Luis Ponce-Cabrera, Alejandro Ponce-Flores, Teresa Flores-Reyes and Ernesto Ponce-Flores

AgriEngineering 2025, 7(4), 119; https://doi.org/10.3390/agriengineering7040119 - 10 Apr 2025

Abstract

A laser-based selective wax ablation method using a 532 nm Nd:YAG laser was developed to improve the foliar uptake efficiency of agrochemicals in citrus leaves. In contrast to conventional applications that suffer major losses, our approach exposes up to 80% of the underlying [...] Read more.

A laser-based selective wax ablation method using a 532 nm Nd:YAG laser was developed to improve the foliar uptake efficiency of agrochemicals in citrus leaves. In contrast to conventional applications that suffer major losses, our approach exposes up to 80% of the underlying epidermis (within the irradiated footprint) with no visible tissue damage, thereby substantially enhancing substance penetration. Efficacy was confirmed using two indicators: (1) A fluorescent glucose analog (2-NBDG) exhibited a radial expansion velocity reaching 0.0105 mm/min in treated areas, enabling rapid phloem transport across an 8 cm distance within just three minutes—an 11,280% improvement over untreated controls. (2) Laser-induced breakdown spectroscopy (LIBS) demonstrated a threefold increase in zinc (Zn) uptake (and over fivefold compared to untreated leaves) when using a Zn-based foliar fertilizer. To assess processing efficiency, we quantified the ablation footprint by combining single-pulse laser shots in a 1 cm-diameter region and found that 23.4% of the total area was fully exposed. This selective, non-invasive approach enables precise targeting, potentially reducing fertilizer and pesticide usage while improving crop health. Beyond citrus, it is readily adaptable to other crops, with integration into orchard or greenhouse spraying systems as a promising path for scale-up. Such versatility highlights the technique’s potential to optimize efficacy, cut input costs, and diminish environmental impact in modern precision agriculture. Full article

(This article belongs to the Topic Emerging Agricultural Engineering Sciences, Technologies, and Applications—2nd Edition)

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18 pages, 357 KiB

Open AccessArticle

Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses

by Gregorius Airlangga, Julius Bata, Oskar Ika Adi Nugroho and Boby Hartanto Pramudita Lim

AgriEngineering 2025, 7(4), 118; https://doi.org/10.3390/agriengineering7040118 - 10 Apr 2025

Abstract

Smart greenhouses rely on precise environmental control to optimize crop yields and resource efficiency. In this study, we propose a novel hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to predict fan actuator states based on environmental data. The hybrid [...] Read more.

Smart greenhouses rely on precise environmental control to optimize crop yields and resource efficiency. In this study, we propose a novel hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to predict fan actuator states based on environmental data. The hybrid model integrates CNNs for spatial feature extraction and LSTMs for temporal dependency modeling, enhanced by a custom activation function and loss function tailored for the problem’s characteristics. The model was trained and evaluated on a comprehensive dataset containing 37,923 samples with 13 environmental features, collected from a smart greenhouse. Experimental results demonstrate the superior performance of the hybrid CNN-LSTM model, achieving an accuracy of 0.9992, precision of 0.9989, recall of 0.9996, and an F1 score of 0.9992, significantly outperforming traditional machine learning methods such as Random Forest and Gradient Boosting, as well as standalone CNN and LSTM architectures. The high recall underscores the model’s reliability in identifying positive actuator states, critical for greenhouse management. This study highlights the importance of hybrid architectures in handling complex spatiotemporal data, offering potential applications beyond greenhouses, such as healthcare monitoring and predictive maintenance. Despite the model’s strengths, limitations include computational complexity and limited interpretability, necessitating future work on optimization and explainability. These findings establish a foundation for integrating deep learning into smart agricultural systems, advancing the automation and efficiency of environmental control mechanisms. Full article

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15 pages, 9032 KiB

Open AccessArticle

Flowering Intensity Estimation Using Computer Vision

by Sergejs Kodors, Imants Zarembo, Ilmars Apeinans, Edgars Rubauskis and Lienite Litavniece

AgriEngineering 2025, 7(4), 117; https://doi.org/10.3390/agriengineering7040117 - 10 Apr 2025

Abstract

Flowering intensity is an important parameter to predict and control fruit yield. However, its estimation is often based on subjective evaluations of fruit growers. This study explores the application of the YOLO framework for flowering intensity estimation. YOLO is a popular computer vision [...] Read more.

Flowering intensity is an important parameter to predict and control fruit yield. However, its estimation is often based on subjective evaluations of fruit growers. This study explores the application of the YOLO framework for flowering intensity estimation. YOLO is a popular computer vision solution for object-detecting tasks. It was applied to detect flowers in different studies. Still, it requires manual annotation of photographs of flowering trees, which is a complex and time-consuming process. It is hard to distinguish individual flowers in photos due to their overlapping and indistinct outlines, false positive flowers in the background, and the density of flowers in panicles. Our experiment shows that the small dataset of images (320 × 320 px) is sufficient to achieve an accuracy of 0.995 and 0.994 mAP@50 for YOLOv9m and YOLOv11m using aggregated mosaic augmentation. The AI-based method was compared with the manual method (flowering intensity estimation, 0–9 scale). The comparison was completed using data analysis and the MobileNetV2 classifier as an evaluation model. The analysis shows that the AI-based method is more effective than the manual method. Full article

(This article belongs to the Special Issue Exploring the Application of Artificial Intelligence and Image Processing in Agriculture)

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14 pages, 1249 KiB

Open AccessArticle

Interface Properties and Droplet Spectra as a Function of Adjuvants and Spray Nozzles

by Caroline Lemes da Silva, João Paulo Arantes Rodrigues da Cunha, Cleyton Batista de Alvarenga and Renan Zampiroli

AgriEngineering 2025, 7(4), 116; https://doi.org/10.3390/agriengineering7040116 - 10 Apr 2025

Abstract

The process of droplet formation during spraying is influenced by several factors, including the nozzle type and the use of adjuvants. This study aimed to investigate the effect of adding adjuvants to spray solutions using different nozzles, with a focus on droplet spectra, [...] Read more.

The process of droplet formation during spraying is influenced by several factors, including the nozzle type and the use of adjuvants. This study aimed to investigate the effect of adding adjuvants to spray solutions using different nozzles, with a focus on droplet spectra, and to examine the impact of the contact angle and the surface tension on this process. The surface tension and contact angle were evaluated using a droplet shape analyzer. The experiment was conducted in a completely randomized design (CRD) using four treatment solutions: water alone and water mixed with three different types of adjuvants, including fatty acid esters (vegetable oil-based), polyether–polymethyl, and polydimethyl-siloxane. The droplet spectra (volume median diameter, relative amplitude, and droplets smaller than 100 µm) were assessed using a particle size analyzer. A CRD with a 4 × 2 factorial scheme was used to assess the effects of the four treatment solutions and two flat-fan nozzles (ULD 120-02 with air induction and LD 110-02 without air induction technology). The polyether–polymethyl considerably reduced the contact angle and surface tension (226% and 180%, respectively, in relation to water). However, it did not homogenize the droplet spectra or reduce the drift risk. The vegetable oil-based adjuvant increased the droplet size when the standard flat-fan nozzle was used. No significant correlation was found between the surface tension and contact angle regarding the droplet spectra parameters. The effect of adjuvants on the droplet spectra was found to be dependent on the nozzle type, which prevents generalizations about the implications of their use. Full article

(This article belongs to the Special Issue Research Progress of Agricultural Machinery Testing)

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18 pages, 5180 KiB

Open AccessArticle

Crop Water Productivity: Within-Field Spatial Variation in Irrigated Alfalfa (Medicago sativa L.)

by Keegan Hammond, Ruth Kerry, Ross Spackman, April Hulet, Bryan G. Hopkins, Matt A. Yost and Neil C. Hansen

AgriEngineering 2025, 7(4), 115; https://doi.org/10.3390/agriengineering7040115 - 10 Apr 2025

Abstract

In this study, alfalfa (Medicago sativa L.) is evaluated for suitability of variable rate irrigation (VRI) by analyzing within-field variation in crop water productivity (CWP) under uniform irrigation. The objectives were to (1) measure within-field variation in crop evapotranspiration (ET), (2) quantify [...] Read more.

In this study, alfalfa (Medicago sativa L.) is evaluated for suitability of variable rate irrigation (VRI) by analyzing within-field variation in crop water productivity (CWP) under uniform irrigation. The objectives were to (1) measure within-field variation in crop evapotranspiration (ET), (2) quantify spatial variability of alfalfa biomass yield, and (3) assess whether a bivariate analysis of CWP and yield could inform VRI management zones. Research was conducted on a 22.6 ha center-pivot irrigated alfalfa field near Rexburg, Idaho, USA, over three harvest intervals (HIs) in 2021 and 2022. Using a water balance method at 66 field points, ET exhibited significant spatial clustering for each HI (p < 0.001 for all HIs), though spatial patterns varied among HIs. Biomass yield, measured via the quadrat method, ranged from 2.1 to 9.7 Mg ha⁻¹, with significant spatial clustering (p < 0.001 for all HIs). The CWP ranged from 0.07 to 0.54 Mg ha⁻¹ cm⁻¹, also showing significant spatial clustering (p < 0.001 for all HIs). Bivariate cluster analysis indicated 12–18% more area of the field was over-watered than under-watered, suggesting potential for optimizing irrigation with VRI. Reducing irrigation in these over-watered zones could improve CWP, supporting alfalfa as a viable candidate for VRI. Full article

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25 pages, 1944 KiB

Open AccessArticle

Growth Curve Models and Clustering Techniques for Studying New Sugarcane Hybrids

by Carlos David Carretillo Moctezuma, María Guzmán Martínez, Flaviano Godínez-Jaimes, José C. García-Preciado, Ramón Reyes Carreto, José Terrones Salgado and Edgar Pérez Arriaga

AgriEngineering 2025, 7(4), 114; https://doi.org/10.3390/agriengineering7040114 - 9 Apr 2025

Abstract

Sugarcane (Saccharum spp.) is a crop of significant industrial and nutritional value, essential for producing various products. Due to its importance, genetic improvement programs involve a rigorous selection process. In this study, growth curve models were used to analyze the maturity curves [...] Read more.

Sugarcane (Saccharum spp.) is a crop of significant industrial and nutritional value, essential for producing various products. Due to its importance, genetic improvement programs involve a rigorous selection process. In this study, growth curve models were used to analyze the maturity curves of 33 hybrids (currently in the adaptability testing phase) and 6 control varieties (MEX 69-290, ITV 92-1424, CP 72-2086, COLMEX 94-8, COLMEX 95-27, RB 85-5113) during both plant and ratoon periods at the Melchor Ocampo Sugar Mill fields in Jalisco, México. With the use of clustering techniques, the materials were classified into four maturity groups: early, early–intermediate, intermediate–late, and late. Hybrids with a larger intercept and smaller slope were classified as having early and early–intermediate maturity. Conversely, hybrids with a smaller intercept and larger slope were classified as having intermediate–late and late maturity. According to the Connectivity and Dunn indexes, the DBSCAN algorithm provides the best clustering structure for materials in the plant cycle, while for the ratoon cycle, the k-means algorithm offers the best clustering structure. This highlights the versatility of each algorithm in the context of hybrid and varietal maturity analysis. These results are crucial for optimizing the productivity and sustainability of the crop, with significant implications for the sugar industry. Full article

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21 pages, 4838 KiB

Open AccessArticle

Scale-Up and Development of a Community Industrial Prototype for Red Palm Oil Production Using Advanced Microwave Technology

by Kamonpan Wongyai, Suttirak Kaewpawong, Dhammanoon Srinoum, Watcharin Kongsawat, Kasidapa Polprasarn, Vikas Rathore and Mudtorlep Nisoa

AgriEngineering 2025, 7(4), 113; https://doi.org/10.3390/agriengineering7040113 - 9 Apr 2025

Abstract

This study presents the development and evaluation of a microwave-assisted prototype for scalable red palm oil production. The prototype, equipped with industrial magnetrons delivering a combined power of 2 kW, is designed to process up to 6 kg of oil palm fruit per [...] Read more.

This study presents the development and evaluation of a microwave-assisted prototype for scalable red palm oil production. The prototype, equipped with industrial magnetrons delivering a combined power of 2 kW, is designed to process up to 6 kg of oil palm fruit per batch. The design, optimized using COMSOL Multiphysics simulations, focused on waveguide configurations and cavity dimensions to ensure uniform energy distribution and minimize hotspots. Performance testing validated the system’s capability to deliver consistent heating across six trays and produce high-quality red palm oil. Results demonstrated a significant reduction in free fatty acid (FFA) content from 20.4% to 2.1% while retaining carotene content within the industrial standard range (558.2 ppm). The Deterioration of Bleachability Index (DOBI) showed a slight reduction but remained within acceptable limits, underscoring the prototype’s ability to maintain oil clarity and processability. Microwave heating effectively inactivated lipase enzymes, reducing FFA and enhancing oil stability, as confirmed by previous studies. The chemical-free process preserved essential nutrients, aligning with sustainability goals. This innovative system provides a scalable, energy-efficient solution for community and industrial applications, offering improved product quality with minimal environmental impact. Future work will focus on optimizing the system further and exploring its applications in broader agricultural processing contexts. Full article

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27 pages, 8684 KiB

Open AccessArticle

Using Drones to Predict Degradation of Surface Drainage on Agricultural Fields: A Case Study of the Atlantic Dykelands

by Mathieu F. Bilodeau, Travis J. Esau, Qamar U. Zaman, Brandon Heung and Aitazaz A. Farooque

AgriEngineering 2025, 7(4), 112; https://doi.org/10.3390/agriengineering7040112 - 8 Apr 2025

Abstract

Excess water in agricultural fields can significantly limit crop productivity. Drone technology offers solutions for identifying and predicting drainage degradation. This study utilized drone-based photogrammetry to create high-resolution elevation models, multispectral imagery for vegetation indices, and flood simulations models to identify zones at [...] Read more.

Excess water in agricultural fields can significantly limit crop productivity. Drone technology offers solutions for identifying and predicting drainage degradation. This study utilized drone-based photogrammetry to create high-resolution elevation models, multispectral imagery for vegetation indices, and flood simulations models to identify zones at risk of poor surface drainage. These models, collected from 2021 to 2023, were used to assess the relationship between poor drainage and corn productivity. The findings revealed a substantial decline in productivity in poorly maintained surface drainage areas, notably a decrease in mean plant height from 1.43 m in 2022 to 0.26 m in flood-prone areas in 2023. Flood-prone zones expanded significantly, from 37% to 61% of the field area between 2022 and 2023, emphasizing the negative cumulative impacts of pre-existing drainage issues. Conversely, fields receiving regular annual maintenance showed an increase in mean plant heights (from 2.23 m to 2.54 m) and NDVI values, reflecting improved drainage conditions. This research demonstrates the effectiveness of drone-derived elevation models for proactively identifying problematic drainage areas, allowing farmers to make informed decisions regarding field maintenance. Implementing these technologies can optimize drainage management practices, enhance agricultural productivity, and increase economic viability in regions that rely on surface drainage. Full article

(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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30 pages, 4911 KiB

Open AccessArticle

In-Field Forage Biomass and Quality Prediction Using Image and VIS-NIR Proximal Sensing with Machine Learning and Covariance-Based Strategies for Livestock Management in Silvopastoral Systems

by Claudia M. Serpa-Imbett, Erika L. Gómez-Palencia, Diego A. Medina-Herrera, Jorge A. Mejía-Luquez, Remberto R. Martínez, William O. Burgos-Paz and Lorena A. Aguayo-Ulloa

AgriEngineering 2025, 7(4), 111; https://doi.org/10.3390/agriengineering7040111 - 8 Apr 2025

Abstract

Controlling forage quality and grazing are crucial for sustainable livestock production, health, productivity, and animal performance. However, the limited availability of reliable handheld sensors for timely pasture quality prediction hinders farmers’ ability to make informed decisions. This study investigates the in-field dynamics of [...] Read more.

Controlling forage quality and grazing are crucial for sustainable livestock production, health, productivity, and animal performance. However, the limited availability of reliable handheld sensors for timely pasture quality prediction hinders farmers’ ability to make informed decisions. This study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. Data collection was conducted using a cellphone camera and a handheld VIS-NIR spectrometer. Feature extraction to build the dataset involved image segmentation, performed using the Mahalanobis distance algorithm, as well as spectral processing to calculate multiple vegetation indices. Machine learning models, including linear regression, LASSO, Ridge, ElasticNet, k-nearest neighbors, and decision tree algorithms, were employed for predictive analysis, achieving high accuracy with R² values ranging from 0.938 to 0.998 in predicting biomass and quality traits. A strategy to achieve high performance was implemented by using four spectral captures and computing the reflectance covariance at NIR wavelengths, accounting for the three-dimensional characteristics of the forage. These findings are expected to advance the development of AI-based tools and handheld sensors particularly suited for silvopastoral systems. Full article

(This article belongs to the Special Issue Exploring the Application of Artificial Intelligence and Image Processing in Agriculture)

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Graphical abstract

22 pages, 6980 KiB

Open AccessArticle

Soil Moisture Spatial Variability and Water Conditions of Coffee Plantation

by Sthéfany Airane dos Santos Silva, Gabriel Araújo e Silva Ferraz, Vanessa Castro Figueiredo, Gislayne Farias Valente, Margarete Marin Lordelo Volpato and Marley Lamounier Machado

AgriEngineering 2025, 7(4), 110; https://doi.org/10.3390/agriengineering7040110 - 8 Apr 2025

Abstract

Remotely piloted aircraft (RPA) are essential in precision coffee farming due to their capability for continuous monitoring, rapid data acquisition, operational flexibility at various altitudes and resolutions, and adaptability to diverse terrain conditions. This study evaluated the soil water conditions in a coffee [...] Read more.

Remotely piloted aircraft (RPA) are essential in precision coffee farming due to their capability for continuous monitoring, rapid data acquisition, operational flexibility at various altitudes and resolutions, and adaptability to diverse terrain conditions. This study evaluated the soil water conditions in a coffee plantation using remotely piloted aircraft to obtain multispectral images and vegetation indices. Fifteen vegetation indices were chosen to evaluate the vigor, water stress, and health of the crop. Soil samples were collected to measure gravimetric and volumetric moisture at depths of 0–10 cm and 10–20 cm. Data were collected at thirty georeferenced sampling points within a 1.2 ha area using GNSS RTK during the dry season (August 2020) and the rainy season (January 2021). The highest correlation (51.57%) was observed between the green spectral band and the 0–10 cm volumetric moisture in the dry season. Geostatistical analysis was applied to map the spatial variability of soil moisture, and the correlation between vegetation indices and soil moisture was evaluated. The results revealed a strong spatial dependence of soil moisture and significant correlations between vegetation indices and soil moisture, highlighting the effectiveness of RPA and geostatistics in assessing water conditions in coffee plantations. In addition to soil moisture, vegetation indices provided information about plant vigor, water stress, and general crop health. Full article

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