Plant-Based, Proximal and Remote Sensing in Orchards and Vineyards — State of the Art, Challenges, Data Fusion and Integration
A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Fruit Production Systems".
Deadline for manuscript submissions: 30 April 2024 | Viewed by 15229
Special Issue Editors
Interests: artificial intelligence; environmental physiology; fruit crops; fruit quality; irrigation; orchard sensing; plant physiology; precision horticulture; remote sensing; robotics; smart farms; spectroscopy; sustainability; traceability
Interests: agronomy; irrigation science; crop physiology; fruit crops; fruit quality; orchard sensing; precision horticulture; remote sensing; robotics; smart farms; spectroscopy; sustainability; traceability
Special Issue Information
Dear Colleagues,
Orchard and vineyard management is rapidly changing as we navigate a fast-paced revolution often referred to as Agriculture 4.0. This revolution is leading to increased automation that requires the use of plant-based, proximal and remote sensors to collect Big Data in orchards and vineyards. Big Data can support fruit, vegetable and nut production in facing global and modern challenges such as the increasing population, climate change, water scarcity, food waste, biosecurity and lack of traceability and credence.
Plant-based or contact sensing (e.g., trunk and fruit dendrometry, near-infrared and fluorescence spectroscopy) obtains the most accurate information on plants’ physiological responses to biotic and abiotic stress at a tree level and on a continuous time scale. Proximal and remote sensing (e.g., machine vision, LiDAR, multispectral and hyperspectral) from ground or aerial platforms and satellites allows for the collection of larger datasets that can provide more detailed spatial information across orchard blocks. Data fusion and integration from different plant-based, proximal and remote sensors and/or data sources remains a practical challenge, but successful attempts can provide the most consistent and accurate data and information about orchards and vineyards.
This Special Issue aims to collect state-of-the-art research on innovative plant-based, proximal and remote sensors used to collect data in orchards and vineyards and on their data fusion and integration to inform orchard management decisions.
Dr. Alessio Scalisi
Dr. Mark Glenn O’Connell
Dr. Ian Goodwin
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.
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Keywords
- artificial intelligence
- big data
- data integration
- data fusion
- fruit crops
- orchard automation
- orchard management
- precision horticulture
- robotics
- smart farms
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Application of a generic decision support system for irrigation management for the case of a wine grape vineyard at northwest Greece
Authors: Ioannis L. Tsirogiannis1,*, Nikolaos Malamos 2 and Penelope Baltzoi1
Affiliation: 1. University of Ioannina, Dept. of Agriculture, Kostakii Campus, 47100 Arta, Greece
2 University of Patras, Dept. of Agriculture, Nea Ktiria Campus, 30200 Messolonghi, Greece
Abstract: In Greece, like other Mediterranean countries, irrigation is the major water user. In this framework the development of operational tools that support decisions and provide recommendations aiming to improved irrigation management and water use efficiency, is of great importance. In this study a web-based participatory decision support system for irrigation management (the DSS hereafter) that operates at the plain of Arta (NW Greece), is evaluated for the case of a commercial wine grape vineyard (Vitis vinifera ‘Vertzami’). The DSS generates recommendations for irrigation applications, based on the outcomes of a water balance model that followed the principles of UN FAO’s paper 56. During the irrigation period of 2021, the whole experimental area of the vineyard was irrigated according to the grower’s experience, while during 2022, it was divided in two plots, one of which was irrigated according to grower’s experience while the other was irrigated based on recommendations from the DSS as calibrated for the specific vineyard. The grower did not had access to the DSS during each season but was briefed about the results at the end of each year. Agro-meteorological conditions, water usage and soil moisture were monitored. The end of day soil moisture time series that were generated by the DSS’s model were compared to those measured by the soil moisture sensors. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), which were used to evaluate the performance of the DSS’, ranged from 2.98% - 3.22% and 3.63% to 4.06% respectively. This fact, documents that the use of the DSS as an alternative to installation of soil moisture sensors at the field is very promising.