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Agronomy
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Application of Deep and Machine Learning in Crop Monitoring and...
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Application of Deep and Machine Learning in Crop Monitoring and Management

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 10683

Special Issue Editors

Dr. Vjekoslav Tadić

E-Mail Website
Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: agricultural engineering; precise agriculture; farming and cropping systems; machines and devices in plant production; pesticide application equipment
Dr. Dorijan Radočaj

E-Mail Website
Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: cropland suitability; land suitability; remote sensing; GIS; predictive mapping; digital soil mapping; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mladen Jurišić

E-Mail Website
Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: crop production; GIS; multicriteria decision making; inventarization of natural resources; agroecosystems and the environment; farming and cropping systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of deep and machine learning in crop monitoring and management has become increasingly important in light of the growing demand for sustainable agricultural practices. While traditional methods provide valuable insights into crop management, the integration of deep and machine learning techniques into existing approaches offers a unique opportunity to improve the efficiency and sustainability of agriculture. By incorporating deep and machine learning analytics, various crop-related parameters such as growth patterns, soil composition and fertilization, crop productivity, climate conditions, pest infestations, and many other issues in modern agriculture can be assessed with greater predictive accuracy. This enables the comprehensive monitoring and management of crops in different agricultural landscapes, from small farms to large plantations. Deep learning algorithms can recognize complex patterns in large data sets when monitoring crops, facilitating informed decision-making processes. By analyzing satellite imagery, sensor data and historical records, or in situ field research data, deep and machine learning models can predict crop yields, identify areas prone to disease outbreaks, and optimize resource allocation for higher productivity.

This Special Issue aims to expand current knowledge on crop monitoring and management assessment using deep and machine learning methods in various agricultural fields. Contributions should cover a broad range of topics that serve as cornerstones for optimizing crop management, with deep and machine learning serving as the primary analytical approaches. Examples of potential topics include precision agriculture, remote sensing applications, environmental impact assessment, climate change in agriculture, biotic and abiotic factors of agricultural production, and other interdisciplinary areas important to crop monitoring and management. We strongly encourage the submission of original research articles and reviews to showcase the versatility of deep and machine learning in crop monitoring and management and to provide professionals worldwide with insights into refining techniques and evaluating criteria in their respective fields.

It is our great pleasure to invite you to the Special Issue "Application of Deep and Machine Learning in Crop Monitoring and Management", which aims to bring together the application of state-of-the-art, efficient, and flexible deep and machine learning methods to determine optimal strategies for crop monitoring and management.

We look forward to receiving your contributions!

Dr. Vjekoslav Tadić
Dr. Dorijan Radočaj
Prof. Dr. Mladen Jurišić
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep and machine learning
  • crop productivity
  • prediction of crop related parameters
  • prediction of biotic and abiotic factors in agricultural production
  • soil composition and fertilization
  • climate change impact of agriculture
  • pest management
  • precision agriculture
  • remote sensing applications
  • convolutional neural networks (CNNs)
  • unmanned aerial vehicles (UAVs)
  • phenotyping
  • data fusion
  • decision support systems

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Published Papers (10 papers)

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Research

23 pages, 8589 KiB  
Article
A Deep Learning-Based Approach to Apple Tree Pruning and Evaluation with Multi-Modal Data for Enhanced Accuracy in Agricultural Practices
by Tong Hai, Wuxiong Wang, Fengyi Yan, Mingyu Liu, Chengze Li, Shengrong Li, Ruojia Hu and Chunli Lv
Agronomy 2025, 15(5), 1242; https://doi.org/10.3390/agronomy15051242 - 20 May 2025
Abstract
A deep learning-based tree pruning evaluation system is proposed in this study, which integrates hyperspectral images, sensor data, and expert system rules. The system aims to enhance the accuracy and robustness of tree pruning tasks through multimodal data fusion and online learning strategies. [...] Read more.
A deep learning-based tree pruning evaluation system is proposed in this study, which integrates hyperspectral images, sensor data, and expert system rules. The system aims to enhance the accuracy and robustness of tree pruning tasks through multimodal data fusion and online learning strategies. Various models, including Mask R-CNN, SegNet, Tiny-Segformer, Box2Mask, CS-Net, SVM, MLP, and Random Forest, were used in the experiments to perform tree segmentation and pruning evaluation, with comprehensive performance assessments conducted. The experimental results demonstrate that the proposed model excels in the tree segmentation task, achieving a precision of 0.94, recall of 0.90, F1 score of 0.92, and mAP@50 and mAP@75 of 0.91 and 0.90, respectively, outperforming other comparative models. These results confirm the effectiveness of multimodal data fusion and dynamic optimization strategies in improving the accuracy of tree pruning evaluation. The experiments also highlight the critical role of sensor data in pruning evaluation, particularly when combined with the online learning strategy, as the model can progressively optimize pruning decisions and adapt to environmental changes. Through this work, the potential and prospects of the deep learning-based tree pruning evaluation system in practical applications are demonstrated. Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)
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22 pages, 3422 KiB  
Article
Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis
by Jun Zhao, Huayu Zhong and Congfeng Wang
Agronomy 2025, 15(5), 1237; https://doi.org/10.3390/agronomy15051237 - 19 May 2025
Abstract
In recent years, the Yellow River Basin has experienced frequent extreme climate events, with an increasing intensity and frequency of droughts, exacerbating regional water scarcity and severely constraining agricultural irrigation efficiency and sustainable water resource utilization. The accurate estimation of reference crop evapotranspiration [...] Read more.
In recent years, the Yellow River Basin has experienced frequent extreme climate events, with an increasing intensity and frequency of droughts, exacerbating regional water scarcity and severely constraining agricultural irrigation efficiency and sustainable water resource utilization. The accurate estimation of reference crop evapotranspiration (ET0) is crucial for developing scientifically sound irrigation strategies and enhancing water resource management capabilities. This study utilized daily scale meteorological data from 31 stations across the Yellow River Basin spanning the period 1960–2023 to develop various machine learning models. The study constructed four machine learning models—random forest (RF), a Support Vector Machine (SVM), Gradient Boosting (GB), and Ridge Regression (Ridge)—using the meteorological variables required by the Priestley–Taylor (PT) and Hargreaves (HG) equations as inputs. These models represent a range of algorithmic structures, from nonlinear ensemble methods (RF, GB) to kernel-based regression (SVR) and linear regularized regression (Ridge). The objective was to comprehensively evaluate their performance and robustness in estimating ET0 under different climatic zones and drought conditions and to compare them with traditional empirical formulas. The main findings are as follows: machine learning models, particularly nonlinear approaches, significantly outperformed the PT and HG methods across all climatic regions. Among them, the RF model demonstrated the highest simulation accuracy, achieving an R2 of 0.77, and reduced the mean daily ET0 estimation error by 0.057 mm/day and 0.076 mm/day compared to the PT and HG models, respectively. Under drought-year scenarios, although all models showed slight performance degradation, nonlinear machine learning models still surpassed traditional formulas, with the R2 of the RF model decreasing marginally from 0.77 to 0.73, indicating strong robustness. In contrast, linear models such as Ridge Regression exhibited greater sensitivity to changes in feature distributions during drought years, with estimation accuracy dropping significantly below that of the PT and HG methods. The results indicate that in data-sparse regions, machine learning approaches with simplified inputs can serve as effective alternatives to empirical formulas, offering superior adaptability and estimation accuracy. This study provides theoretical foundations and methodological support for regional water resource management, agricultural drought mitigation, and climate-resilient irrigation planning in the Yellow River Basin. Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)
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23 pages, 2054 KiB  
Article
A Spatiotemporal Attention-Guided Graph Neural Network for Precise Hyperspectral Estimation of Corn Nitrogen Content
by Feiyu Lu, Boming Zhang, Yifei Hou, Xiao Xiong, Chaoran Dong, Wenbo Lu, Liangxue Li and Chunli Lv
Agronomy 2025, 15(5), 1041; https://doi.org/10.3390/agronomy15051041 - 26 Apr 2025
Viewed by 223
Abstract
A hyperspectral maize nitrogen content prediction model is proposed, integrating a dynamic spectral–spatiotemporal attention mechanism with a graph neural network, with the aim of enhancing the accuracy and stability of nitrogen estimation. Across multiple experiments, the proposed method achieved outstanding performance on the [...] Read more.
A hyperspectral maize nitrogen content prediction model is proposed, integrating a dynamic spectral–spatiotemporal attention mechanism with a graph neural network, with the aim of enhancing the accuracy and stability of nitrogen estimation. Across multiple experiments, the proposed method achieved outstanding performance on the test set, with R2=0.93, RMSE of 0.35, and MAE of 0.48, significantly outperforming comparative models including SVM, RF, ResNet, and ViT. In experiments conducted across different growth stages, the best performance was observed during the grain-filling stage, where R2 reached 0.96. In terms of accuracy, recall, and precision, the proposed model exhibited an average improvement exceeding 15%, demonstrating strong adaptability to temporal variation and generalization across spatial conditions. These results provide robust technical support for large-scale, nondestructive nitrogen monitoring in agricultural applications. Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)
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20 pages, 14318 KiB  
Article
Multi-Feature Driver Variable Fusion Downscaling TROPOMI Solar-Induced Chlorophyll Fluorescence Approach
by Jinrui Fan, Xiaoping Lu, Guosheng Cai, Zhengfang Lou and Jing Wen
Agronomy 2025, 15(1), 133; https://doi.org/10.3390/agronomy15010133 - 8 Jan 2025
Viewed by 754
Abstract
Solar-induced chlorophyll fluorescence (SIF), as a direct indicator of vegetation photosynthesis, offers a more accurate measure of plant photosynthetic dynamics than traditional vegetation indices. However, the current SIF satellite products have low spatial resolution, limiting their application in fine-scale agricultural research. To address [...] Read more.
Solar-induced chlorophyll fluorescence (SIF), as a direct indicator of vegetation photosynthesis, offers a more accurate measure of plant photosynthetic dynamics than traditional vegetation indices. However, the current SIF satellite products have low spatial resolution, limiting their application in fine-scale agricultural research. To address this, we leveraged MODIS data at a 1 km resolution, including bands b1, b2, b3, and b4, alongside indices such as the NDVI, EVI, NIRv, OSAVI, SAVI, LAI, FPAR, and LST, covering October 2018 to May 2020 for Shandong Province, China. Using the Random Forest (RF) model, we downscaled SIF data from 0.05° to 1 km based on invariant spatial scaling theory, focusing on the winter wheat growth cycle. Various machine learning models, including CNN, Stacking, Extreme Random Trees, AdaBoost, and GBDT, were compared, with Random Forest yielding the best performance, achieving R2 = 0.931, RMSE = 0.052 mW/m2/nm/sr, and MAE = 0.031 mW/m2/nm/sr for 2018–2019 and R2 = 0.926, RMSE = 0.058 mW/m2/nm/sr, and MAE = 0.034 mW/m2/nm/sr for 2019–2020. The downscaled SIF products showed a strong correlation with TanSIF and GOSIF products (R2 > 0.8), and consistent trends with GPP further confirmed the reliability of the 1 km SIF product. Additionally, a time series analysis of Shandong Province’s wheat-growing areas revealed a strong correlation (R2 > 0.8) between SIF and multiple vegetation indices, underscoring its utility for regional crop monitoring. Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)
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37 pages, 18088 KiB  
Article
ACO-TSSCD: An Optimized Deep Multimodal Temporal Semantic Segmentation Change Detection Approach for Monitoring Agricultural Land Conversion
by Henggang Zhang, Kaiyue Luo, Alim Samat, Chenhui Zhu and Tianyu Jiao
Agronomy 2024, 14(12), 2909; https://doi.org/10.3390/agronomy14122909 - 5 Dec 2024
Viewed by 1026
Abstract
With the acceleration of urbanization in agricultural areas and the continuous changes in land-use patterns, the transformation of agricultural land presents complexity and dynamism, which puts higher demands on precise monitoring. And most existing monitoring methods are constrained by limited spatial and temporal [...] Read more.
With the acceleration of urbanization in agricultural areas and the continuous changes in land-use patterns, the transformation of agricultural land presents complexity and dynamism, which puts higher demands on precise monitoring. And most existing monitoring methods are constrained by limited spatial and temporal resolution, high computational demands, and challenges in distinguishing complex land cover types. These limitations hinder their ability to effectively detect rapid and subtle land use changes, particularly in areas experiencing rapid urban expansion, where their shortcomings become more pronounced. To address these challenges, this study presents a multimodal deep learning framework using a temporal semantic segmentation change detection (TSSCD) model optimized with ant colony optimization (ACO) to detect and analyze agricultural land conversion in Zhengzhou City, a major grain-producing area in China. This model utilizes Landsat 7/8 imagery and Sentinel-2 satellite imagery from 2003 to 2023 to capture the spatiotemporal transformation of cropland driven by urban expansion, infrastructure development, and population changes over the last two decades. The optimized TSSCD model achieves superior classification accuracy, with the kappa coefficient improving from 0.871 to 0.892, spatial F1 score from 0.903 to 0.935, and temporal F1 score from 0.848 to 0.879, indicating its effectiveness in identifying complex land-use changes. The significant spatiotemporal variation characteristics of agricultural land conversion in Zhengzhou City from 2003 to 2023 were revealed through the TSSCD model, with transformations initially concentrated near Zhengzhou’s urban core and expanding outward, particularly to the east and north. These results highlight the effectiveness of remote sensing and deep learning techniques in monitoring agricultural land conversion. Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)
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17 pages, 7822 KiB  
Article
A New Winter Wheat Crop Segmentation Method Based on a New Fast-UNet Model and Multi-Temporal Sentinel-2 Images
by Mohamad M. Awad
Agronomy 2024, 14(10), 2337; https://doi.org/10.3390/agronomy14102337 - 10 Oct 2024
Cited by 2 | Viewed by 1297
Abstract
Mapping and monitoring crops are the most complex and difficult tasks for experts processing and analyzing remote sensing (RS) images. Classifying crops using RS images is the most expensive task, and it requires intensive labor, especially in the sample collection phase. Fieldwork requires [...] Read more.
Mapping and monitoring crops are the most complex and difficult tasks for experts processing and analyzing remote sensing (RS) images. Classifying crops using RS images is the most expensive task, and it requires intensive labor, especially in the sample collection phase. Fieldwork requires periodic visits to collect data about the crop’s physiochemical characteristics and separating them using the known conventional machine learning algorithms and remote sensing images. As the problem becomes more complex because of the diversity of crop types and the increase in area size, sample collection becomes more complex and unreliable. To avoid these problems, a new segmentation model was created that does not require sample collection or high-resolution images and can successfully distinguish wheat from other crops. Moreover, UNet is a well-known Convolutional Neural Network (CNN), and the semantic method was adjusted to become more powerful, faster, and use fewer resources. The new model was named Fast-UNet and was used to improve the segmentation of wheat crops. Fast-UNet was compared to UNet and Google’s newly developed semantic segmentation model, DeepLabV3+. The new model was faster than the compared models, and it had the highest average accuracy compared to UNet and DeepLabV3+, with values of 93.45, 93.05, and 92.56 respectively. Finally, new datasets of time series NDVI images and ground truth data were created. These datasets, and the newly developed model, were made available publicly on the Web. Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)
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15 pages, 23629 KiB  
Article
Machine Learning Methods for Evaluation of Technical Factors of Spraying in Permanent Plantations
by Vjekoslav Tadić, Dorijan Radočaj and Mladen Jurišić
Agronomy 2024, 14(9), 1977; https://doi.org/10.3390/agronomy14091977 - 1 Sep 2024
Cited by 1 | Viewed by 999
Abstract
Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm2, droplet diameter, and [...] Read more.
Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm2, droplet diameter, and drift. The studies were conducted with two different types of sprayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, and code 03), working speed (6 and 8 km h−1), and spraying norm (250–400 L h−1). The airflow of both sprayers was adjusted to the plantation leaf mass and the working pressure was set for each repetition separately. A method using water-sensitive paper and a digital image analysis was used to collect data on coverage factors. The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest predictive value for the properties of number of droplets per cm2 (axial = 69.1%; radial = 66.0%), droplet diameter (axial = 30.6%; radial = 38.2%), and area coverage (axial = 24.6%; radial = 34.8%). Spraying norm had the greatest predictive value for area coverage (axial = 43.3%; radial = 26.9%) and drift (axial = 72.4%; radial = 62.3%). Greater coverage of the treated area and a greater number of droplets were achieved with the radial sprayer, as well as less drift. The accuracy of the machine learning model for the prediction of the treated surface showed a satisfactory accuracy for most properties (R2 = 0.694–0.984), except for the estimation of the droplet diameter for an axial sprayer (R2 = 0.437–0.503). Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)
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17 pages, 4257 KiB  
Article
Prediction and Classification of Phenol Contents in Cnidium officinale Makino Using a Stacking Ensemble Model in Climate Change Scenarios
by Hyunjo Lee, Hyun Jung Koo, Kyeong Cheol Lee, Yoojin Song, Won-Kyun Joo and Cheol-Joo Chae
Agronomy 2024, 14(8), 1766; https://doi.org/10.3390/agronomy14081766 - 12 Aug 2024
Cited by 1 | Viewed by 839
Abstract
Recent studies have focused on using big-data-based machine learning to address the effects of climate change scenarios on the production and quality of medicinal plants. Challenges relating to data collection can hinder the analysis of key feature variables that affect the quality of [...] Read more.
Recent studies have focused on using big-data-based machine learning to address the effects of climate change scenarios on the production and quality of medicinal plants. Challenges relating to data collection can hinder the analysis of key feature variables that affect the quality of medicinal plants. In the study presented herein, we analyzed feature variables that affect the phenolic content of Korean Cnidium officinale Makino (C. officinale Makino) under different climate change scenarios. We applied different climate change scenarios based on environmental information obtained from Yeongju city, Gyeongsangbuk-do, Republic of Korea, and cultivated C. officinale Makino to collect data. The collected data included 3237, 75, and 45 records, and data augmentation was performed to address this data imbalance. We designed a function based on the DPPH value to set the phenolic content grade in C. officinale Makino and proposed a stacking ensemble model for predicting the total phenol contents and classifying the phenolic content grades. The regression model in the performance evaluation presented an improvement of 6.23–7.72% in terms of the MAPE; in comparison, the classification model demonstrated a 2.48–3.34% better performance in terms of accuracy. The classification accuracy was >0.825 when classifying phenol content grades using the predicted total phenol content values from the regression model, and the area under the curve values of the model indicated high model fitness (0.987–0.981). We plan to identify the key feature variables for the optimal cultivation of C. officinale Makino and explore the relationships among these feature variables. Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)
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31 pages, 9913 KiB  
Article
A Precise Plot-Level Rice Yield Prediction Method Based on Panicle Detection
by Junshuo Wei, Xin Tian, Weiqi Ren, Rui Gao, Zeguang Ji, Qingming Kong and Zhongbin Su
Agronomy 2024, 14(8), 1618; https://doi.org/10.3390/agronomy14081618 - 24 Jul 2024
Cited by 1 | Viewed by 2249
Abstract
Accurately estimating rice yield is essential for ensuring global food security, enhancing agricultural productivity, and promoting agricultural economic growth. This study constructed a dataset of rice panicles at different growth stages and combined it with an attention mechanism and the YOLOv8 network to [...] Read more.
Accurately estimating rice yield is essential for ensuring global food security, enhancing agricultural productivity, and promoting agricultural economic growth. This study constructed a dataset of rice panicles at different growth stages and combined it with an attention mechanism and the YOLOv8 network to propose the YOLOv8s+LSKA+HorNet rice panicle detection and counting model, based on a drone remote sensing platform. Using the panicle count data collected by this model, along with the thousand-grain weight, number of grains per panicle, and actual yield data from a rice nitrogen gradient experimental field, various machine learning models were trained to ultimately propose a field-level rapid rice yield estimation model, RFYOLO. The experimental results show that the rice panicle detection and counting model can achieve an average precision (AP) of 98.0% and a detection speed of 20.3 milliseconds. The final yield estimation model achieved a prediction R2 value of 0.84. The detection and counting model significantly reduced missed and duplicate detections of rice panicles. Additionally, this study not only enhanced the model’s generalization ability and practicality through algorithmic innovation but also verified the impact of yield data range on the stability of the estimation model through the rice nitrogen gradient experiment. This is significant for early rice yield estimation and helping agricultural producers make more informed planting decisions. Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)
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15 pages, 4333 KiB  
Article
Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks
by Olivera Ećim-Đurić, Mihailo Milanović, Aleksandra Dimitrijević-Petrović, Zoran Mileusnić, Aleksandra Dragičević and Rajko Miodragović
Agronomy 2024, 14(6), 1147; https://doi.org/10.3390/agronomy14061147 - 27 May 2024
Cited by 2 | Viewed by 1256
Abstract
In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately [...] Read more.
In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions. Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)
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