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Agronomy
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Smart Farming Technologies for Sustainable Agriculture—2nd Edition
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Smart Farming Technologies for Sustainable Agriculture—2nd Edition

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 7104

Special Issue Editors

Dr. Andreas Stylianou

E-Mail Website
Guest Editor
Agricultural Research Institute, Rural Development Section, P.O. Box 22016, Nicosia 1516, Cyprus
Interests: economics of agricultural production; sustainability assessment of farming systems; adoption of agricultural innovations; smart farming technologies
Special Issues, Collections and Topics in MDPI journals
Dr. George Adamides

E-Mail Website
Guest Editor
Agricultural Research Institute, Rural Development Section, P.O. Box 22016, Nicosia 1516, Cyprus
Interests: human-robot interaction; IoT in agriculture; smart farming; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Damianos Neocleous

E-Mail Website
Guest Editor
Agricultural Research Institute, Natural Resources and Environment Section, P.O. Box 22016, Nicosia 1516, Cyprus
Interests: plant physiology; plant nutrition; hydroponics; sustainable intensive agriculture; precision irrigation and nutrient management; smart farming approaches
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Christopher Brewster

E-Mail Website
Guest Editor
Netherlands Organisation for Applied Scientific Research, Anna van Buerenplein 1, NL-2595 DA The Hague, The Netherlands
Interests: sustainable agriculture and food systems; ICT in agrifood; environmental and social impact of technologies in agrifood; data science and AI in sustainable agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart farming (SF) involves a variety of technologies, such as mapping and recording technologies (satellite and unmanned aerial vehicle imagery, multiple types of sensors, and Internet of Things-connected weather stations), farm management information systems or decision support systems, and technologies such as variable rate applications and agricultural robots. SF has been suggested as a promising driver for achieving higher sustainability performance without compromising the environment or human health. SF technologies may potentially lead to more efficient use of inputs (e.g., fertilizers, pesticides, irrigation, labor), to the reduction in production costs, to the minimization of the environmental footprint, and to improved product quality. In the light of climate breakdown and the need for adaptation and mitigation policies, the adoption of SF technologies is now more than ever an imperative. However, the adoption rate, especially by small- and medium-sized farms, is still low or fragmented, and the tools provided by SF have not yet moved into mainstream farm management. This low uptake is due to various factors, including the low perceived usefulness. There is a need to provide evidence of the actual impacts of SF technologies for agricultural sustainability and to persuade farmers of the actual benefits of SF. The objective of this Special Issue is to identify the (positive or negative) impacts of SF technologies on the economic, environmental, and social sustainability of farming systems, including agri-food value chains, and, thereby, enable informed choices by farmers. We invite you to contribute to this Special Issue by submitting original research articles, reviews, and case studies that provide scientific evidence of the actual impacts of SF technologies on the environmental, economic, and social sustainability of farms. Contributions related to the development of traceability systems based on recorded data from SF technologies are also welcome insofar as they highlight the impacts on sustainability. We look forward to receiving your contributions on the broad topic of this Special Issue in order to foster discussions within this important emerging field.

Dr. Andreas Stylianou
Dr. George Adamides
Dr. Damianos Neocleous
Prof. Dr. Christopher Brewster
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

  • smart farming technologies
  • precision agriculture
  • digitalization
  • sensors
  • internet of things
  • decision support systems
  • indicators
  • sustainable agriculture and agri-food value chains
  • sustainability assessment
  • sustainability impacts

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Related Special Issue

Published Papers (6 papers)

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Research

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18 pages, 6352 KiB  
Article
Mapping the Main Phenological Spatiotemporal Changes of Summer Maize in the Huang-Huai-Hai Region Based on Multiple Remote Sensing Indices
by Dianchen Han, Peijuan Wang, Yang Li, Yuanda Zhang and Jianping Guo
Agronomy 2025, 15(5), 1182; https://doi.org/10.3390/agronomy15051182 - 13 May 2025
Viewed by 198
Abstract
Accurately extracting the phenology of maize, one of the three major staple crops, is crucial for assessing regional suitability under climate change, optimizing field management, predicting yield fluctuations, and ensuring food security. This study compares and validates the accuracy of various vegetation indices, [...] Read more.
Accurately extracting the phenology of maize, one of the three major staple crops, is crucial for assessing regional suitability under climate change, optimizing field management, predicting yield fluctuations, and ensuring food security. This study compares and validates the accuracy of various vegetation indices, including the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), solar-induced chlorophyll fluorescence (SIF), and kernel NDVI (kNDVI), in extracting the phenological phases of summer maize at the sixth leaf (V6), tasseling (VT), and maturity (R6). Additionally, explainable machine learning methods were employed to elucidate how climate and stress factors influence the phenological sequences of summer maize. The results show that compared to NDVI and EVI, SIF and kNDVI are more suitable for extracting the summer maize phenological phase. SIF achieved the highest phenological extraction precision at the V6 and R6 phases, with root mean square errors (RMSEs) of 7.86 and 8.22 days, respectively. kNDVI provided the highest extraction accuracy for the VT phase, with an RMSE of 5 days. SHapley Additive exPlanations (SHAP) analysis revealed that temperature and radiation are the primary meteorological factors influencing maize phenology in the study area. Regarding stress factors, drought and heat stress delayed phenology at the V6 and VT phases, while heat stress prior to maturity accelerated summer maize maturation. In conclusion, this study reveals the potential of emerging vegetation indices for extracting maize phenology, offering both data and theoretical support for regional crop adaptability assessments. Full article
(This article belongs to the Special Issue Smart Farming Technologies for Sustainable Agriculture—2nd Edition)
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16 pages, 6406 KiB  
Article
A Shooting Distance Adaptive Crop Yield Estimation Method Based on Multi-Modal Fusion
by Dan Xu, Ba Li, Guanyun Xi, Shusheng Wang, Lei Xu and Juncheng Ma
Agronomy 2025, 15(5), 1036; https://doi.org/10.3390/agronomy15051036 - 25 Apr 2025
Viewed by 324
Abstract
To address the low estimation accuracy of deep learning-based crop yield image recognition methods under untrained shooting distances, this study proposes a shooting distance adaptive crop yield estimation method by fusing RGB and depth image information through multi-modal data fusion. Taking strawberry fruit [...] Read more.
To address the low estimation accuracy of deep learning-based crop yield image recognition methods under untrained shooting distances, this study proposes a shooting distance adaptive crop yield estimation method by fusing RGB and depth image information through multi-modal data fusion. Taking strawberry fruit fresh weight as an example, RGB and depth image data of 348 strawberries were collected at nine heights ranging from 70 to 115 cm. First, based on RGB images and shooting height information, a single-modal crop yield estimation model was developed by training a convolutional neural network (CNN) after cropping strawberry fruit images using the relative area conversion method. Second, the height information was expanded into a data matrix matching the RGB image dimensions, and multi-modal fusion models were investigated through input-layer and output-layer fusion strategies. Finally, two additional approaches were explored: direct fusion of RGB and depth images, and extraction of average shooting height from depth images for estimation. The models were tested at two untrained heights (80 cm and 100 cm). Results showed that when using only RGB images and height information, the relative area conversion method achieved the highest accuracy, with R2 values of 0.9212 and 0.9304, normalized root mean square error (NRMSE) of 0.0866 and 0.0814, and mean absolute percentage error (MAPE) of 0.0696 and 0.0660 at the two untrained heights. By further incorporating depth data, the highest accuracy was achieved through input-layer fusion of RGB images with extracted average height from depth images, improving R2 to 0.9475 and 0.9384, reducing NRMSE to 0.0707 and 0.0766, and lowering MAPE to 0.0591 and 0.0610. Validation using a developed shooting distance adaptive crop yield estimation platform at two random heights yielded MAPE values of 0.0813 and 0.0593. This model enables adaptive crop yield estimation across varying shooting distances, significantly enhancing accuracy under untrained conditions. Full article
(This article belongs to the Special Issue Smart Farming Technologies for Sustainable Agriculture—2nd Edition)
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11 pages, 1967 KiB  
Article
A Decision Support System for Irrigation Scheduling Using a Reduced-Size Pan
by Georgios Nikolaou, Damianos Neocleous, Efstathios Evangelides and Evangelini Kitta
Agronomy 2025, 15(4), 848; https://doi.org/10.3390/agronomy15040848 - 28 Mar 2025
Viewed by 316
Abstract
An automatic, weight-based, small 20 cm diameter pan was used for real-time calculations of evaporation and precipitation in a semiarid environment. The water evaporated from the evaporimeter (EP) was found to be a significant predictor of evapotranspiration (ETO; r [...] Read more.
An automatic, weight-based, small 20 cm diameter pan was used for real-time calculations of evaporation and precipitation in a semiarid environment. The water evaporated from the evaporimeter (EP) was found to be a significant predictor of evapotranspiration (ETO; r2 = 0.84), which was calculated with the Penman–Monteith (P-M) equation by retrieving climatic data from a weather station. The results revealed seasonal variations of the pan coefficient (KP; dimensionless), with a mean value estimated at 0.84 (±0.16). Validation of ETO measurements using a calibrated regression model (ETO = 0.831*EP + 0.025), against the P-M equation indicated a high correlation coefficient (r2 = 0.99, slope of the regression line of 0.9). The present paper evaluates and discusses the potential of using a reduced-size pan for real-time monitoring of water evaporation and precipitation, proposing an open-source irrigation decision support system. Full article
(This article belongs to the Special Issue Smart Farming Technologies for Sustainable Agriculture—2nd Edition)
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29 pages, 8291 KiB  
Article
A Novel Transpiration Drought Index for Winter Wheat in the Huang-Huai-Hai Region, China: A Process-Based Framework Incorporating Improved Crop Water Supply–Demand Dynamics
by Qianchuan Mi, Zhiguo Huo, Meixuan Li, Lei Zhang, Rui Kong, Fengyin Zhang, Yi Wang and Yuxin Huo
Agronomy 2025, 15(3), 679; https://doi.org/10.3390/agronomy15030679 - 11 Mar 2025
Viewed by 619
Abstract
Monitoring agricultural drought is crucial for mitigating yield losses in winter wheat, especially in the Huang-Huai-Hai (HHH) region of China. Current drought indices often fall short in accurately representing the water supply–demand dynamics for crops, neglect irrigation practices, and overemphasize drought intensity rather [...] Read more.
Monitoring agricultural drought is crucial for mitigating yield losses in winter wheat, especially in the Huang-Huai-Hai (HHH) region of China. Current drought indices often fall short in accurately representing the water supply–demand dynamics for crops, neglect irrigation practices, and overemphasize drought intensity rather than its evolution and overall impact. To address these concerns, we developed a novel transpiration drought index utilizing the Water Balance for Winter Wheat (WBWW) model. This index integrated variations in atmospheric conditions, soil moisture conditions, crop resistance, and irrigation practices to enhance the evaluation of water supply and demand dynamics. The WBWW model was initially validated against field transpiration measurements, achieving an R2 of 0.7573, thereby confirming its reliability for subsequent analyses. To create a mechanistic understanding of crop water supply and demand, we adopted the reduction rate of actual and potential transpiration to identify drought events and constructed joint probability distributions of drought duration and severity using copulas. This led to the development of the Winter Wheat Drought Assessment Index (WDAI). The grade threshold for the WDAI was established based on historical drought data from the HHH region through a series of statistical threshold determination methods. Our findings showed that the WDAI successfully identified 87.36% of drought samples according to their recorded grades, with 97.13% within one grade of historical records. Comparative analyses with retained regional data and existing indices—the Crop Water Deficit Index (CWDI) and the Relative Soil Moisture Index (RSMI)—further demonstrated its effectiveness. Our study represents a robust tool for dynamic drought monitoring in the HHH region and offers critical insights into agricultural irrigation practices. Full article
(This article belongs to the Special Issue Smart Farming Technologies for Sustainable Agriculture—2nd Edition)
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21 pages, 8642 KiB  
Article
Spectral Variability Analysis of Lupinus mutabilis Sweet Under Nanofertilizer and Chelate Application Through Spectroscopy and Unmanned Aerial Vehicle (UAV) Multispectral Images
by Izar Sinde-González, Erika Murgueitio-Herrera, César E. Falconí, Mariluz Gil-Docampo and Theofilos Toulkeridis
Agronomy 2025, 15(2), 469; https://doi.org/10.3390/agronomy15020469 - 14 Feb 2025
Viewed by 1182
Abstract
Lupin is an Andean legume that has gained importance in Ecuador due to the protein content in its grain. Nonetheless, in recent times the production of lupin has been affected by inadequate nutritional management. In order to avoid such circumstances, the current study [...] Read more.
Lupin is an Andean legume that has gained importance in Ecuador due to the protein content in its grain. Nonetheless, in recent times the production of lupin has been affected by inadequate nutritional management. In order to avoid such circumstances, the current study spectrally analyzed lupin cultivation under the application of nanofertilizers and Fe and Zn chelates, within two controlled trials, using a radiometer spectrum, an active crop sensor and a multispectral sensor mounted on a UAV. Vegetation indices were generated and subsequently statistically analyzed using ANOVA and Tukey tests. In the field trial, the treatments lacked an indication of significant improvements, while in the greenhouse trial, the nanofertilizer treatments indicated better results compared to the control treatments. However, it was also determined that the application of nanofertilizers at a concentration of 540 ppm demonstrated significant efficiency in greenhouse conditions, which could not be achieved in the field. Furthermore, the chelate treatment presented a certain degree of toxicity for the plant. Full article
(This article belongs to the Special Issue Smart Farming Technologies for Sustainable Agriculture—2nd Edition)
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Review

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28 pages, 2541 KiB  
Review
Intelligent Rapid Asexual Propagation Technology—A Novel Aeroponics Propagation Approach
by Lingdi Tang, Ain-ul-Abad Syed, Ali Raza Otho, Abdul Rahim Junejo, Mazhar Hussain Tunio, Li Hao, Mian Noor Hussain Asghar Ali, Sheeraz Aleem Brohi, Sohail Ahmed Otho and Jamshed Ali Channa
Agronomy 2024, 14(10), 2289; https://doi.org/10.3390/agronomy14102289 - 5 Oct 2024
Viewed by 2833
Abstract
Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m2) of high-quality propagule (free from contamination) at a relatively low [...] Read more.
Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m2) of high-quality propagule (free from contamination) at a relatively low cost in a small space. They are also used for plant preservation. This review article aims to provide potential applications for regeneration and clonal propagation. Plant propagation using advanced agrotechnology, such as aeroponics, is becoming increasingly popular among academics and industrialists. The advancement of asexual aeroponic propagation has been achieved through advancements in monitoring and control systems using IoT and smart sensor technology. New sensor technology systems have gained substantial interest in agriculture in recent years. It is used in agriculture to precisely arrange various operations and objectives while harnessing limited resources with minimal human intervention. Modern intelligent technologies and control systems simplify sensor data collection, making it more efficient than manual data collection, which can be slow and prone to errors. Specific ambient variables like temperature, humidity, light intensity, stock solution concentrations (nutrient water), EC (electrical conductivity), pH values, CO2 content, and atomization parameters (frequency and interval) are collected more effectively through these systems. The use of intelligent technologies provides complete control over the system. When combined with IoT, it aids in boosting crop quality and yield while also lowering production costs and providing data directly to tablets and smartphones in aeroponic propagation systems. It can potentially increase the system’s productivity and usefulness compared to the older manual monitoring and operating methods. Full article
(This article belongs to the Special Issue Smart Farming Technologies for Sustainable Agriculture—2nd Edition)
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