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AgriEngineering
Special Issues
Agrometeorology Tools and Applications for Precision Farming
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Agrometeorology Tools and Applications for Precision Farming

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Sensors Technology and Precision Agriculture".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 15262

Special Issue Editors

Dr. N. R. Patel

E-Mail Website
Guest Editor
Indian Institute of Remote Sensing, Dehradun, India
Interests: geoinformatics applications in agriculture; satellite agrometeorology; earth observation for drought assessment; terrestrial biogeochemical cycles and land surface processes and EO inputs
Dr. Raj Setia

E-Mail Website
Guest Editor
Punjab Remote Sensing Centre, Ludhiana, India
Interests: precision agriculture; soil science; remote and proximal sensing; GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

In today’s world, information and technology-based agricultural management is required to improve crop production efficiency by adjusting farming inputs to specific conditions within each field; this is called precision farming. Agrometeorological decision making in agricultural operations for growing crops or crops endangered by pests, diseases, and/or other environmental disasters requires improved weather forecasts (timely/high resolution) and crop monitoring with the best possible user required accuracies. Agrometeorology, as an integrated science of bilogical and physical phenomenon, finds close linkage with remote sensing and allied geospatial technologies that are being widely used in agricultural decision making. Recent development of agrometeorology tools and methods aimed at precision farming relies on automatic weather station network, wireless sensor network, geospatial technologies (satellites, GIS, GNSS) (OPTICAL/SAR/LIDAR), variable rate technology, UAVS, ground-based sensing, high resolution weather forecast, dynamic crop models, and artificial Inteligience (AI) and machine learning (ML) tools. The timely and precise weather and crop monitoring facilitates the identification of management zones, within-field yield variability, field scale soil, and crop health monitoring, and also aid in variable rate application of inputs and management strategies, etc. Therefore, precision farming techniques are required in the face of ongoing issues such as climate change and sutainabale management of crop, water, and soil resources.

The potential of precision agrculture for smart framing could be visualied through imageries taken from high reolsution satellite imageries, Unmanned Aerial Vehicles (UAVs), or any other platform; meteorological data from weather stations/satellites; and farmers practices with smart phones. All of these can be coupled with Geogrpahic Information System (GIS) and Global Navigation Satellite Systems (GNSS). 

The aim of this Special Issue is to foster advances in agrometeorology and precision farming for a range of practical applications in agriculture which include, but are not limited to, the following topics: 

  • Agrometeorological indices and climatic data tools determining growth, development, and yields of important crops for precision farming;
  • Crop simulation models (CSM) as a tool for assessing within-field yield variability and crop management zones;
  • Integrated use of remote sensing and crop model to support precision agriculture;
  • Advancing use of geospatial technologies, weather forecast, and AI/ML tools to support precise drought/salinity/fertility mapping and forecasting;
  • Agrometeorological programs and software tools to derive farm scale process-based agrometeorological indicators from high resolution images at finer scale;
  • Development and application of remote sensing and weather based time-series tools to predict crop phenology/pest-disease outbreak;
  • Development of Decision Support System/Softwares in agrometeorology for smart farming
  • Variable application of inputs aided with digital agriculture;
  • UAVs, tower-mounted, and air-borne sensors (optical, thermal, and microwave) for precise crop stressor (water, nutrient, pests/disease, and salinity) moniotoring;
  • Application of advanced satellite sensors (SAR/GNSS/LIDAR) for precise soil moisture and crop growth monitoring;
  • Application of agro-hydrological models and software for precise agricultural water management;
  • Impact and adaptation of agrometorological services and/or precison farming on the livelihood of farmers.

The contributions on above themes will be useful for appropriate policy environments which are given as one of the building blocks of agrometeorological services, in which initial and boundary conditions are determined for solving well identified problems in the livelihood of farmers through such services.

We are looking for new and innovative scientific studies, and encourage submissions that involve new findings.

Dr. N. R. Patel
Dr. Raj Setia
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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

  • agriculture
  • agrometeorology
  • GIS
  • GPS
  • modelling
  • precision farming
  • remote sensing

Published Papers (4 papers)

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Research

14 pages, 5224 KiB  
Article
Performance Evaluation of a Typical Low-Cost Multi-Frequency Multi-GNSS Device for Positioning and Navigation in Agriculture—Part 2: Dynamic Testing
by Nang Van Nguyen and Wonjae Cho
AgriEngineering 2023, 5(1), 127-140; https://doi.org/10.3390/agriengineering5010008 - 11 Jan 2023
Cited by 2 | Viewed by 2120
Abstract
Dynamic performance of a GNSS (Global Navigation Satellite System) positioning device (PD) is of interest to end users of satellite-based auto-guidance systems (AGS) for agricultural vehicles, especially when a low-cost PD is used. This study evaluated the overall dynamic performance in an agricultural [...] Read more.
Dynamic performance of a GNSS (Global Navigation Satellite System) positioning device (PD) is of interest to end users of satellite-based auto-guidance systems (AGS) for agricultural vehicles, especially when a low-cost PD is used. This study evaluated the overall dynamic performance in an agricultural environment for the PDs paired with multi-frequency multi-GNSS receivers and antennae, including low-cost and legacy ones. The dynamic performance was evaluated in terms of reacquisition time, heading accuracy, positioning accuracy, and guidance accuracy at short and medium baselines of RTK (Real Time Kinematic) positioning. The dynamic testing was conducted using an instrumented vehicle with test PDs to perform the test runs on agricultural fields and asphalt roads. The test results proved that the dynamic performance of the low-cost PD was not inferior to that of the legacy one and accurate enough to be used as a positioning sensor for the auto guidance of agricultural vehicles. It could reacquire an RTK-fix solution within 4 s after a 3-s GNSS signal blockage and achieve sub-degree-level accuracy of the heading measurement for agricultural vehicles traveling in an open-sky environment. Furthermore, the low-cost PD could obtain a 4-cm-level dynamic RTK positioning accuracy and agricultural vehicle guidance accuracy of less than 3 cm. The techniques and test results from this study provided additional guidelines for determining the overall dynamic performance of the GNSS PDs and AGSs on an agricultural tractor. Full article
(This article belongs to the Special Issue Agrometeorology Tools and Applications for Precision Farming)
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22 pages, 4740 KiB  
Article
In-Season Wheat Yield Forecasting at High Resolution Using Regional Climate Model and Crop Model
by S. M. Kirthiga and N. R. Patel
AgriEngineering 2022, 4(4), 1054-1075; https://doi.org/10.3390/agriengineering4040066 - 30 Oct 2022
Cited by 5 | Viewed by 2786
Abstract
In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the efforts to establish a framework towards quantifying the crop yield [...] Read more.
In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the efforts to establish a framework towards quantifying the crop yield prediction at regional scales are limited, we investigated the utility of combining crop model with the regional weather prediction model to forecast winter wheat yields over space. The exercise was performed for various lead-times in the regions of Punjab and Haryana for the years 2008–2009. A numerical weather prediction (NWP) model was used to generate micro-meteorological variables at different lead times (1-week, 2-weeks, 3-weeks and 5-weeks) ahead of crop harvest and used within the CERES-Wheat crop simulation model gridded framework at a spatial resolution of 10 km. Various scenarios of the yield forecasts were verified with district-wide reported yield values. Average deviations of −12 to 3% from the actual district-wise wheat yields were observed across the lead times. The 3-weeks-ahead yield forecasts yielded a maximum agreement index of 0.86 with a root mean squared error (RMSE) of 327.75 kg/ha and a relative deviation of −5.35%. The critical crop growth stages were found to be highly sensitive to the errors in the weather forecast, and thus made a huge impact on the predicted crop yields. The 5-weeks-ahead weather forecasts generated anomalous meteorological data during flowering and grain-filling crop growth stages, and thus had the highest negative impact on the simulated yields. The agreement index of the 5-week-ahead forecasts was 0.41 with an RMSE of 415.15 kg ha−1 and relative deviation of −2.77 ± 5.01. The proposed methodology showed significant forecast skill for extended space and time scale crop yield forecasting, offering scope for further research and practical applicability. Full article
(This article belongs to the Special Issue Agrometeorology Tools and Applications for Precision Farming)
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24 pages, 6065 KiB  
Article
Impacts and Climate Change Adaptation of Agrometeorological Services among the Maize Farmers of West Tamil Nadu
by Punnoli Dhanya, Vellingiri Geethalakshmi, Subbiah Ramanathan, Kandasamy Senthilraja, Punnoli Sreeraj, Chinnasamy Pradipa, Kulanthaisamy Bhuvaneshwari, Mahalingam Vengateswari, Ganesan Dheebakaran, Sembanan Kokilavani, Ramasamy Karthikeyan and Nagaranai Karuppasamy Sathyamoorthy
AgriEngineering 2022, 4(4), 1030-1053; https://doi.org/10.3390/agriengineering4040065 - 25 Oct 2022
Cited by 4 | Viewed by 3995
Abstract
Climate change is often linked with record-breaking heavy or poor rainfall events, unprecedented storms, extreme day and night time temperatures, etc. It may have a marked impact on climate-sensitive sectors and associated livelihoods. Block-level weather forecasting is a new-fangled dimension of agrometeorological services [...] Read more.
Climate change is often linked with record-breaking heavy or poor rainfall events, unprecedented storms, extreme day and night time temperatures, etc. It may have a marked impact on climate-sensitive sectors and associated livelihoods. Block-level weather forecasting is a new-fangled dimension of agrometeorological services (AAS) in the country and is getting popularized as a climate-smart farming strategy. Studies on the economic impact of these microlevel advisories are uncommon. Agromet advisory services (AAS) play a critical role as an early warning service and preparedness among the maize farmers in the Parambikulam–Aliyar Basin, as this area still needs to widen and deepen its AWS network to reach the village level. In this article, the responses of the maize farmers of Parambikulam–Aliyar Basin on AAS were analyzed. AAS were provided to early and late Rabi farmers during the year 2020–2022. An automatic weather station was installed at the farmers’ field to understand the real-time weather. Forecast data from the India Meteorological Department (IMD) were used to provide agromet advisory services. Therefore, the present study deserves special focus. Social media and other ICT tools were used for AAS dissemination purposes. A crop simulation model (CSM), DSSAT4.7cereal maize, was used for assessing maize yield in the present scenario and under the elevated GHGs scenario under climate change. Our findings suggest that the AAS significantly supported the farmers in sustaining production. The AAS were helpful for the farmers during the dry spells in the late samba (2021–2022) to provide critical irrigation and during heavy rainfall events at the events of harvest during early and late Rabi (2021–22). Published research articles on the verification of weather forecasts from South India are scanty. This article also tries to understand the reliability of forecasts. Findings from the verification suggest that rainfall represented a fairly good forecast for the season, though erratic, with an accuracy score or HI score of 0.77 and an HK score of 0.60, and the probability of detection (PoD) of hits was found to be 0.91. Verification shows that the forecasted relative humidity observed showed a fairly good correlation, with an R2 value of 0.52. These findings suggest that enhancing model forecast accuracy can enhance the reliability and utility of AAS as a climate-smart adaptation option. This study recommends that AAS can act as a valuable input to alleviate the impacts of hydrometeorological disasters on maize crop production in the basin. There is a huge demand for quality weather forecasts with respect to accuracy, resolution, and lead time, which is increasing across the country. Externally funded research studies such as ours are an added advantage to bridge the gap in AAS dissemination to a great extent. Full article
(This article belongs to the Special Issue Agrometeorology Tools and Applications for Precision Farming)
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18 pages, 3786 KiB  
Article
Evaluating the Expediency of Smartphone Applications for Indian Farmers and Other Stakeholders
by Soundharya Sivakumar, Gowryparvathy Bijoshkumar, Athulya Rajasekharan, Vaishnavi Panicker, Sivaraj Paramasivam, V. S. Manivasagam and Sudheesh Manalil
AgriEngineering 2022, 4(3), 656-673; https://doi.org/10.3390/agriengineering4030042 - 22 Jul 2022
Cited by 4 | Viewed by 4030
Abstract
Smartphone application usage has increased exponentially over the past decade. The potentiality of smartphone applications as a tool for various decision-making processes is not fully explored, especially in the field of agriculture. This work systematically evaluates smartphone applications developed by research institutes and [...] Read more.
Smartphone application usage has increased exponentially over the past decade. The potentiality of smartphone applications as a tool for various decision-making processes is not fully explored, especially in the field of agriculture. This work systematically evaluates smartphone applications developed by research institutes and non-profit organizations and made available to Indian agriculture stakeholders, who have the world’s largest user base. The study analyzed 25 smartphone applications developed for the agriculture and allied sectors available to the Indian farming community. The usability, accessibility, frequency of updates, user ratings, and number of downloads of smartphone applications are systematically evaluated. Furthermore, this article assesses the divergence between existing smartphone applications and the needs of agricultural stakeholders. This research necessitates the importance of systematic evaluation of digital applications available to the end-users and offers guidelines to application developers, researchers, and policymakers on the potential shortcomings of prevailing smartphone applications and warrants features for future smartphone applications. Full article
(This article belongs to the Special Issue Agrometeorology Tools and Applications for Precision Farming)
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