Field Phenotyping for Precise Crop Management

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: 15 February 2026 | Viewed by 1169

Special Issue Editors


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Guest Editor
Field Crops Program, Institute for Food and Agricultural Research and Technology (IRTA), 251981 Lleida, Spain
Interests: plant phenotyping; remote sensing; UAV; cereal; wheat; drought; RGB; thermal imaging; multispectral sensors
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Guest Editor Assistant
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence (UNIFI), Piazzale delle Cascine 18, 50144 Florence, Italy
Interests: image-based plant phenotyping; image analysis; abiotic stress; low-cost phenotyping platform; crop monitoring; 3D modeling and reconstruction

Special Issue Information

Dear Colleagues,

High-throughput field phenotyping has become essential not only in plant breeding but also in agricultural monitoring and decision-making. In recent decades, significant technological advances have enabled the evaluation of crop traits across various spatial, temporal, and organizational scales. The literature reports a wide range of phenotyped traits, measured either directly or indirectly, using diverse platforms, sensors, indices, and data-processing tools. For instance, the normalized difference vegetation index (NDVI) is one of the most widely used indices, commonly employed as an indirect indicator of biomass and canopy vigor, although its specific purpose can vary depending on experimental objectives. Applications span from variety selection in breeding programs to agronomic decision-making, particularly in evaluating management strategies aimed at optimizing crop productivity and enhancing sustainability.

Beyond the development of new sensing and imaging technologies, there is a growing need to streamline the entire data pipeline, from field-level acquisition to downstream processing, analysis, and interpretation. This involves efficient transfer, storage, and integration into analytical software environments. The ability to automate dataflow, standardize processing routines, and scale up analyses is becoming critical in the usability of field phenotyping outputs.

This Special Issue aims to address the main challenges and opportunities of the diverse range of applications in field phenotyping. We are particularly interested in contributions that explore whether phenotyping remains a bottleneck in agricultural research, how phenotypic traits relate to genotypic adaptation under different management conditions, and how data integration across time and scale can be achieved. Additionally, we welcome studies focused on managing large volumes of high-resolution data, developing scalable analytical frameworks, and presenting novel data extraction protocols or custom software solutions that enhance the interoperability and reproducibility of phenotyping workflows.

Dr. Adrian Gracia-Romero
Guest Editor

Dr. Riccardo Rossi
Guest Editor Assistant

Manuscript Submission Information

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Keywords

  • high-throughput phenotyping
  • crop monitoring
  • sensor technology
  • trait analysis
  • genotype-environment interaction
  • UAV
  • image analysis
  • precision agriculture
  • IoT

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Published Papers (1 paper)

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Research

20 pages, 14182 KB  
Article
Automated 3D Phenotyping of Maize Plants: Stereo Matching Guided by Deep Learning
by Juan Zapata-Londoño, Juan Botero-Valencia, Ítalo A. Torres, Erick Reyes-Vera and Ruber Hernández-García
Agriculture 2025, 15(24), 2573; https://doi.org/10.3390/agriculture15242573 - 12 Dec 2025
Abstract
Automated three-dimensional plant phenotyping is an essential tool for non-destructive analysis of plant growth and structure. This paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants. The system incorporates an automatic detection stage for [...] Read more.
Automated three-dimensional plant phenotyping is an essential tool for non-destructive analysis of plant growth and structure. This paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants. The system incorporates an automatic detection stage for the object of interest using deep learning techniques to delimit the region of interest (ROI) corresponding to the plant. The Semi-Global Block Matching (SGBM) algorithm is applied to the detected region to compute the disparity map and generate a partial three-dimensional representation of the plant structure. The ROI delimitation restricts the disparity calculation to the plant area, reducing processing of the background and optimizing computational resource use. The deep learning-based detection stage maintains stable foliage identification even under varying lighting conditions and shadowing, ensuring consistent depth data across different experimental conditions. Overall, the proposed system integrates detection and disparity estimation into an efficient processing flow, providing an accessible alternative for automated three-dimensional phenotyping in agricultural environments. Full article
(This article belongs to the Special Issue Field Phenotyping for Precise Crop Management)
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