Call for Contributions to a Special Issue of Turkish Journal of Agriculture and Forestry – Remote and proximal sensing for mapping in-field leaf chlorophyll content and monitoring crop growth, development, health, and yields
Call for Contributions to a Special Issue of Turkish Journal of Agriculture and Forestry
Remote and proximal sensing for mapping in-field leaf chlorophyll content and monitoring crop growth, development, health, and yields.
Guest Editors
- Ephrem Habyarimana, Chief Scientist at CREA Research Center for Cereal and Industrial Crops, via di Corticella 133, 40128 Bologna, Italy (Email: ephrem.habyarimana@crea.gov.it)
- Faheem Shehzad Baloch, Department of Field Crops, Faculty of Agricultural and Natural Science, Bolu Abant Izzet Baysal University, Bolu 14030, Turkey (Email: balochfaheem13@gmail.com)
Genotypic and spatial information on crop’s leaf chlorophyll concentration can be used for plant nursery screening in the process of crop improvement, and is central for monitoring plant health, productivity and managing nutrient optimization programs in agricultural systems. Quantifying chlorophyll in plants leaves is also vital to understanding plants response to climate change and other biotic and abiotic adversities across diverse plant ecosystems. The chlorophyll are the main key molecules in this area of research as they display intrinsic properties that facilitate the conversion of absorbed solar irradiance into stored chemical energy, and are therefore associated with the plant photosynthetic capacity and primary productivity.
Remote and proximal sensing offer a means for measuring genotypic chlorophyll content and in-field mapping of plant chlorophyll content over a variety of spatial and temporal scales. This Special Issue is going to help garner state-of-the-art research and technologies to retrieve and model the chlorophyll that existed in plants at the leaf and canopy levels across a variety of agricultural settings for several applications, particularly in crop breeding and precision agriculture.
We welcome research works on chlorophyll content retrieval approaches using different tools and parametric and non-parametric algorithms, including machine learning and artificial intelligence to solve current challenges associated with chlorophyll retrieval and mapping using remote and proximal sensing technologies. Both theoretical and application-oriented studies are invited. Information can be derived from several tools including but not limited to handheld and field-bound sensors, uncrewed aerial vehicles (drones), operational satellites such as Sentinel-2 constellations and other hyperspectral missions.
For review and opinion papers, please discuss a tentative outline with the editors of the special issue. Article will be published online following acceptance. The deadline for contributions is December 2020 whereas target date for the printed issue to be published is May 2021.