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Agronomy
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Data-Driven Agriculture: Remote Sensing and Machine Learning for Sustainable Farming...
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Data-Driven Agriculture: Remote Sensing and Machine Learning for Sustainable Farming Practices

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 5620

Special Issue Editors

Dr. Sara Álvarez

E-Mail Website
Guest Editor
Department of Horticultural and Woody Crops, Instituto Tecnológico Agrario de Castilla y León (ITACYL), Crta Burgos Km 119, CP 47071 Valladolid, Spain
Interests: deficit irrigation; plant physiology; ornamental plants; stress physiology; evapotranspiration; salinity; water relations; tree nut crops; intrinsic water use efficiency
Special Issues, Collections and Topics in MDPI journals
Dr. Sergio Vélez

E-Mail Website
Guest Editor
Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
Interests: NDVI; vineyard; drone; UAV; satellite; sentinel; remote sensing; thermal; water; landsat; vegetation index; multispectral; hyperspectral; spectroradiometer; UAS; NIR; RGB; infrared; woody crops; grape; vine; precision agriculture; sensor; DSS; SfM; LiDAR

Special Issue Information

Dear Colleagues,

In the endeavor to achieve sustainable agriculture, the integration of remote sensing (RS) and machine learning (ML) technologies presents an invaluable opportunity. These burgeoning technologies enable a data-driven approach to agricultural practices, ensuring the optimized utilization of resources, and a substantial reduction in environmental degradation. Remote sensing facilitates the real-time monitoring of crop and soil conditions from a distance, while machine learning provides the tools to analyze these vast datasets, uncovering patterns and insights that can guide sustainable agricultural decisions.

The nexus of RS and ML not only supports the monitoring and management of agricultural resources but also plays a critical role in addressing challenges like pest infestations, water scarcity, and nutrient management. By developing predictive models, it is conceivable to anticipate issues before they escalate, allowing for timely interventions. Furthermore, these technologies aid in the precision application of inputs such as water, fertilizers, and pesticides, ensuring that the agricultural footprint is minimized while productivity is enhanced.

This Special Issue invites original, quantitative, and comprehensive studies exploring the application of remote sensing and machine learning in sustainable agriculture. Submissions spanning a wide array of crops and agricultural systems, under field or controlled environmental conditions, are welcomed. We are particularly interested in manuscripts addressing the following topics: (1) the development and validation of RS and ML models for crop monitoring as well as pest and disease detection; (2) the application of RS and ML in precision irrigation and water resource management; (3) the utilization of RS and ML for soil health assessment and nutrient management; (4) the assessment of the economic and environmental impacts of RS and ML applications on agriculture; and (5) case studies showcasing the successful integration of RS and ML in sustainable agricultural practices.

Dr. Sara Álvarez
Dr. Sergio Vélez
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

  • remote sensing
  • machine learning
  • sustainable agriculture
  • digital agriculture
  • precision irrigation
  • crop monitoring
  • pest and disease detection
  • water resource management
  • soil and nutrient assessment
  • predictive modeling

Published Papers (5 papers)

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Research

16 pages, 2774 KiB  
Article
Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards
by Sergio Vélez, Enrique Barajas, José Antonio Rubio, Dimas Pereira-Obaya and José Ramón Rodríguez-Pérez
Agronomy 2024, 14(3), 634; https://doi.org/10.3390/agronomy14030634 - 21 Mar 2024
Viewed by 697
Abstract
This study explores spectroscopy in the 350 to 2500 nm range for detecting powdery mildew (Erysiphe necator) in grapevine leaves, crucial for precision agriculture and sustainable vineyard management. In a controlled experimental vineyard setting, the spectral reflectance on leaves with varying [...] Read more.
This study explores spectroscopy in the 350 to 2500 nm range for detecting powdery mildew (Erysiphe necator) in grapevine leaves, crucial for precision agriculture and sustainable vineyard management. In a controlled experimental vineyard setting, the spectral reflectance on leaves with varying infestation levels was measured using a FieldSpec 4 spectroradiometer during July and September. A detailed assessment was conducted following the guidelines recommended by the European and Mediterranean Plant Protection Organization (EPPO) to quantify the level of infestation; categorising leaves into five distinct grades based on the percentage of leaf surface area affected. Subsequently, spectral data were collected using a contact probe with a tungsten halogen bulb connected to the spectroradiometer, taking three measurements across different areas of each leaf. Partial Least Squares Regression (PLSR) analysis yielded coefficients of determination R2 = 0.74 and 0.71, and Root Mean Square Errors (RMSEs) of 12.1% and 12.9% for calibration and validation datasets, indicating high accuracy for early disease detection. Significant spectral differences were noted between healthy and infected leaves, especially around 450 nm and 700 nm for visible light, and 1050 nm, 1425 nm, 1650 nm, and 2250 nm for the near-infrared spectrum, likely due to tissue damage, chlorophyll degradation and water loss. Finally, the Powdery Mildew Vegetation Index (PMVI) was introduced, calculated as PMVI = (R755 − R675)/(R755 + R675), where R755 and R675 are the reflectances at 755 nm (NIR) and 675 nm (red), effectively estimating disease severity (R2 = 0.7). The study demonstrates that spectroscopy, combined with PMVI, provides a reliable, non-invasive method for managing powdery mildew and promoting healthier vineyards through precision agriculture practices. Full article
(This article belongs to the Special Issue Data-Driven Agriculture: Remote Sensing and Machine Learning for Sustainable Farming Practices)
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17 pages, 2341 KiB  
Article
Comparative Performance of Aerial RGB vs. Ground Hyperspectral Indices for Evaluating Water and Nitrogen Status in Sweet Maize
by Milica Colovic, Anna Maria Stellacci, Nada Mzid, Martina Di Venosa, Mladen Todorovic, Vito Cantore and Rossella Albrizio
Agronomy 2024, 14(3), 562; https://doi.org/10.3390/agronomy14030562 - 11 Mar 2024
Viewed by 984
Abstract
This study analyzed the capability of aerial RGB (red-green-blue) and hyperspectral-derived vegetation indices to assess the response of sweet maize (Zea mays var. saccharata L.) to different water and nitrogen inputs. A field experiment was carried out during 2020 by using both [...] Read more.
This study analyzed the capability of aerial RGB (red-green-blue) and hyperspectral-derived vegetation indices to assess the response of sweet maize (Zea mays var. saccharata L.) to different water and nitrogen inputs. A field experiment was carried out during 2020 by using both remote RGB images and ground hyperspectral sensor data. Physiological parameters (i.e., leaf area index, relative water content, leaf chlorophyll content index, and gas exchange parameters) were measured. Correlation and multivariate data analysis (principal component analysis and stepwise linear regression) were performed to assess the strength of the relationships between eco-physiological measured variables and both RGB indices and hyperspectral data. The results revealed that the red-edge indices including CIred-edge, NDRE and DD were the best predictors of the maize physiological traits. In addition, stepwise linear regression highlighted the importance of both WI and WI:NDVI for prediction of relative water content and crop temperature. Among the RGB indices, the green-area index showed a significant contribution in the prediction of leaf area index, stomatal conductance, leaf transpiration and relative water content. Moreover, the coefficients of correlation between studied crop variables and GGA, NDLuv and NDLab were higher than with the hyperspectral indices measured at the ground level. The findings confirmed the capacity of selected RGB and hyperspectral indices to evaluate the water and nitrogen status of sweet maize and provided opportunity to expand experimentation on other crops, diverse pedo-climatic conditions and management practices. Hence, the aerially collected RGB vegetation indices might represent a cost-effective solution for crop status assessment. Full article
(This article belongs to the Special Issue Data-Driven Agriculture: Remote Sensing and Machine Learning for Sustainable Farming Practices)
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23 pages, 4178 KiB  
Article
A Machine Learning-Based High-Resolution Soil Moisture Mapping and Spatial–Temporal Analysis: The mlhrsm Package
by Yuliang Peng, Zhengwei Yang, Zhou Zhang and Jingyi Huang
Agronomy 2024, 14(3), 421; https://doi.org/10.3390/agronomy14030421 - 22 Feb 2024
Viewed by 879
Abstract
Soil moisture is a key environmental variable. There is a lack of software to facilitate non-specialists in estimating and analyzing soil moisture at the field scale. This study presents a new open-sourced R package mlhrsm, which can be used to generate Machine [...] Read more.
Soil moisture is a key environmental variable. There is a lack of software to facilitate non-specialists in estimating and analyzing soil moisture at the field scale. This study presents a new open-sourced R package mlhrsm, which can be used to generate Machine Learning-based high-resolution (30 to 500 m, daily to monthly) soil moisture maps and uncertainty estimates at selected sites across the contiguous USA at 0–5 cm and 0–1 m. The model is based on the quantile random forest algorithm, integrating in situ soil sensors, satellite-derived land surface parameters (vegetation, terrain, and soil), and satellite-based models of surface and rootzone soil moisture. It also provides functions for spatial and temporal analysis of the produced soil moisture maps. A case study is provided to demonstrate the functionality to generate 30 m daily to weekly soil moisture maps across a 70-ha crop field, followed by a spatial–temporal analysis. Full article
(This article belongs to the Special Issue Data-Driven Agriculture: Remote Sensing and Machine Learning for Sustainable Farming Practices)
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26 pages, 2602 KiB  
Article
Fostering Agricultural Transformation through AI: An Open-Source AI Architecture Exploiting the MLOps Paradigm
by Antonio Carlos Cob-Parro, Yerhard Lalangui and Raquel Lazcano
Agronomy 2024, 14(2), 259; https://doi.org/10.3390/agronomy14020259 - 25 Jan 2024
Viewed by 1408
Abstract
As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge [...] Read more.
As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge this “land gap” and mitigate the environmental implications of agricultural land use. Typically, the problem with using AI in such agricultural sectors is the need for more specific infrastructure to enable developers to design AI and ML engineers to deploy these AIs. It is, therefore, essential to develop dedicated infrastructures to apply AI models that optimize resource extraction in the agricultural sector. This article presents an infrastructure for the execution and development of AI-based models using open-source technology, and this infrastructure has been optimized and tuned for agricultural environments. By embracing the MLOps culture, the automation of AI model development processes is promoted, ensuring efficient workflows, fostering collaboration among multidisciplinary teams, and promoting the rapid deployment of AI-driven solutions adaptable to changing field conditions. The proposed architecture integrates state-of-the-art tools to cover the entire AI model lifecycle, enabling efficient workflows for data scientists and ML engineers. Considering the nature of the agricultural field, it also supports diverse IoT protocols, ensuring communication between sensors and AI models and running multiple AI models simultaneously, optimizing hardware resource utilization. Surveys specifically designed and conducted for this paper with professionals related to AI show promising results. These findings demonstrate that the proposed architecture helps close the gap between data scientists and ML engineers, easing the collaboration between them and simplifying their work through the whole AI model lifecycle. Full article
(This article belongs to the Special Issue Data-Driven Agriculture: Remote Sensing and Machine Learning for Sustainable Farming Practices)
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18 pages, 5870 KiB  
Article
Prediction of Stem Water Potential in Olive Orchards Using High-Resolution Planet Satellite Images and Machine Learning Techniques
by Simone Pietro Garofalo, Vincenzo Giannico, Leonardo Costanza, Salem Alhajj Ali, Salvatore Camposeo, Giuseppe Lopriore, Francisco Pedrero Salcedo and Gaetano Alessandro Vivaldi
Agronomy 2024, 14(1), 1; https://doi.org/10.3390/agronomy14010001 - 19 Dec 2023
Cited by 1 | Viewed by 1095
Abstract
Assessing plant water status accurately in both time and space is crucial for maintaining satisfactory crop yield and quality standards, especially in the face of a changing climate. Remote sensing technology offers a promising alternative to traditional in situ measurements for estimating stem [...] Read more.
Assessing plant water status accurately in both time and space is crucial for maintaining satisfactory crop yield and quality standards, especially in the face of a changing climate. Remote sensing technology offers a promising alternative to traditional in situ measurements for estimating stem water potential (Ψstem). In this study, we carried out field measurements of Ψstem in an irrigated olive orchard in southern Italy during the 2021 and 2022 seasons. Water status data were acquired at midday from 24 olive trees between June and October in both years. Reflectance data collected at the time of Ψstem measurements were utilized to calculate vegetation indices (VIs). Employing machine learning techniques, various prediction models were developed by considering VIs and spectral bands as predictors. Before the analyses, both datasets were randomly split into training and testing datasets. Our findings reveal that the random forest model outperformed other models, providing a more accurate prediction of olive water status (R2 = 0.78). This is the first study in the literature integrating remote sensing and machine learning techniques for the prediction of olive water status in order to improve olive orchard irrigation management, offering a practical solution for estimating Ψstem avoiding time-consuming and resource-intensive fieldwork. Full article
(This article belongs to the Special Issue Data-Driven Agriculture: Remote Sensing and Machine Learning for Sustainable Farming Practices)
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