Plant phenotyping has garnered major attention in recent years, leading to developing new strategies to measure and assess plant traits of interest. For data acquisition of large fields, devices and sensors are required that deliver detailed and reproducible temporal and spatial information on the cultivated crop. This work proposes the potential use of low-cost light drones for in-field phenotyping applications on cereal crops. The proposed method allows to obtain precise measurements of color and height of the plants for the individual plots. The method is based on a color calibration algorithm (TPS-3D interpolating function) and a 3D ortho image reconstruction. The method has been applied on an experimental field with durum and soft wheat parcels obtaining information on real color (with an error lower than 12/256) and height for each single plot.
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http://dx.doi.org/10.1007/978-1-0716-1201-9_18 | DOI Listing |
MethodsX
December 2024
Department of Industrial Engineering, University of Padua, Padua, Italy.
Recent research highlights advancements in collecting Artificial Light at Night (ALAN) data using radiosondes on stratospheric balloons, revealing a need for enhanced in-flight image stabilization. This paper proposes a twofold approach: Firstly, it introduces a design concept for a high-resolution image acquisition and stabilization system for aerial instruments (e.g.
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December 2024
Department of Computer Science, University of Verona, Italy.
Multispectral imaging (MSI) is a technique used to inspect materials properties in different domains, ranging from industrial to medical and cultural heritage and, recently, precision agriculture. Even though several MSI solutions are already commercially available, the research community is working to optimize multispectral cameras in terms of performance and cost. Systems for the agricultural field are usually very compact, combined with drones for large areas acquisition.
View Article and Find Full Text PDFSensors (Basel)
December 2024
North Willamette Research and Extension Center, Oregon State University, Aurora, OR 97002, USA.
Incorporating data-driven technologies into agriculture presents a promising approach to optimizing crop production, especially in regions dependent on irrigation, where escalating heat waves and droughts driven by climate change pose increasing challenges. Recent advancements in sensor technology have introduced diverse methods for assessing irrigation needs, including meteorological sensors for calculating reference evapotranspiration, belowground sensors for measuring plant available water, and plant sensors for direct water status measurements. Among these, infrared thermometry stands out as a non-destructive remote sensing method for monitoring transpiration, with significant potential for integration into drone- or satellite-based models.
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December 2024
College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Powering and communicating with wearable devices on bio-interfaces is challenging due to strict weight, size, and resource constraints. This study presents a sunflower-like plant-wearable sensing device that harnesses solar energy, achieving complete energy self-sustainability for long-term monitoring of plant sap flow, a crucial indicator of plant health. It features foldable solar panels along with all essential flexible electronic components, resulting in a compact system that is lightweight enough for small plants.
View Article and Find Full Text PDFAnim Cogn
November 2024
Department of Neurology, Shenzhen Hospital, Southern Medical University, Shenzhen, 518110, China.
Previous studies suggest that social learning in bumblebees can occur through second-order conditioning, with conspecifics functioning as first-order reinforcers. However, the behavioural mechanisms underlying bumblebees' acquisition of socially learned associations remain largely unexplored. Investigating these mechanisms requires detailed quantification and analysis of the observation process.
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