Pregled bibliografske jedinice broj: 1237536
Field-Based High-Throughput Phenotyping Using Newly Developed Proximal Sensor Device
Field-Based High-Throughput Phenotyping Using Newly Developed Proximal Sensor Device // International Conference on Smart Systems and Technologies 2022 (SST 2022)
Osijek, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2022. doi:10.1109/sst55530.2022.9954672 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1237536 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Field-Based High-Throughput Phenotyping Using Newly
Developed Proximal Sensor Device
Autori
Simic, Domagoj ; Galic, Vlatko ; Spisic, Josip ; Mazur, Maja ; Ledencan, Tatjana ; Zdunic, Zvonimir
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
ISBN
978-1-6654-8215-8
Skup
International Conference on Smart Systems and Technologies 2022 (SST 2022)
Mjesto i datum
Osijek, Hrvatska, 19.10.2022. - 21.10.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
proximal sensing , Agriculture 4.0 , chlorophyll fluorescence , NDVI , maize
Sažetak
To cope with changing climate and increasing human population, agricultural production requires more data on plant status. High-throughput phenotyping represents new avenue of data collection, increasing throughput in both time and space. We developed a low-cost proximal sensing node retrieving reads at six wavelengths in red (610 and 680 nm), near infra-red (730 and 760 nm) and infrared (810 and 860 nm), intended to capture important plant reflectance indices and fluorescence signals. The reads were collected in fields of maize, barley and wheat and coupled with reads from a commercial multispectral sensing device and a handheld fluorimeter, measuring plant photosynthetic efficiency in a JIP-test framework. Reads of our sensing node were standardized by calculating vegetation indexes (VI). Strong correlations were observed between VIs retrieved from our sensor and NDVI measured by a commercial device, reaching R2>0.91 . Correlation analysis also showed strong link between VI810610 and primary electron transport, vanishing between VIs and higher order photosynthetic reactions. Collection of meaningful ecophysiological data facilitates plant state retrieval in scales from seconds to months (temporal throughput) with increasing spatial density (spatial throughput). Proximal sensing nodes combined with novel communication devices (Internet of Things), emerging phenotyping technologies and methods (high-throughput phenotyping) and data science frameworks (machine learning) show promise in transition to Agriculture 4.0/5.0.
Izvorni jezik
Engleski
POVEZANOST RADA
Ustanove:
Poljoprivredni institut Osijek,
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek
Profili:
Tatjana Ledenčan
(autor)
Maja Mazur
(autor)
Domagoj Šimić
(autor)
Vlatko Galić
(autor)
Josip Spišić
(autor)
Zvonimir Zdunić
(autor)