A comparative estimation of maize leaf water content using machine learning techniques and unmanned aerial vehicle (UAV)-based proximal and remotely sensed data (Record no. 64434)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02997nab|a22003857a|4500 |
| 001 - CONTROL NUMBER | |
| control field | 64434 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MX-TxCIM |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230630193512.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 202110s2021||||sz |||p|op||||00||0|eng|d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER | |
| International Standard Serial Number | 2072-4292 (Online) |
| 024 8# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | https://doi.org/10.3390/rs13204091 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MX-TxCIM |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ndlovu, H.S. |
| 9 (RLIN) | 24381 |
| 245 12 - TITLE STATEMENT | |
| Title | A comparative estimation of maize leaf water content using machine learning techniques and unmanned aerial vehicle (UAV)-based proximal and remotely sensed data |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Basel (Switzerland) : |
| Name of publisher, distributor, etc. | MDPI, |
| Date of publication, distribution, etc. | 2021. |
| 500 ## - GENERAL NOTE | |
| General note | Peer review |
| 500 ## - GENERAL NOTE | |
| General note | Open Access |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Determining maize water content variability is necessary for crop monitoring and in developing early warning systems to optimise agricultural production in smallholder farms. However, spatially explicit information on maize water content, particularly in Southern Africa, remains elementary due to the shortage of efficient and affordable primary sources of suitable spatial data at a local scale. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit, near-real-time information for determining the maize crop water status at farm scale. Therefore, this study evaluated the utility of UAV-derived multispectral imagery and machine learning techniques in estimating maize leaf water indicators: equivalent water thickness (EWT), fuel moisture content (FMC), and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising the maize water indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC, and SLA were derived from the random forest regression (RFR) algorithm with an rRMSE of 3.13%, 1%, and 3.48%, respectively. Additionally, EWT and FMC yielded the highest predictive performance and were the most optimal indicators of maize leaf water content. The findings are critical towards developing a robust and spatially explicit monitoring framework of maize water status and serve as a proxy of crop health and the overall productivity of smallholder maize farms. |
| 546 ## - LANGUAGE NOTE | |
| Language note | Text in English |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Precision agriculture |
| 9 (RLIN) | 4619 |
| Source of heading or term | AGROVOC |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Crop monitoring |
| 9 (RLIN) | 11128 |
| Source of heading or term | AGROVOC |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Unmanned aerial vehicles |
| 9 (RLIN) | 11401 |
| Source of heading or term | AGROVOC |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Smallholders |
| 9 (RLIN) | 1763 |
| Source of heading or term | AGROVOC |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 9 (RLIN) | 11127 |
| Source of heading or term | AGROVOC |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Odindi, J. |
| 9 (RLIN) | 24382 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sibanda, M. |
| 9 (RLIN) | 23029 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mutanga, O. |
| 9 (RLIN) | 19859 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Clulow, A.D. |
| 9 (RLIN) | 23030 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chimonyo, V.G.P. |
| Field link and sequence number | 001712688 |
| Miscellaneous information | Sustainable Intensification Program |
| -- | Sustainable Agrifood Systems |
| 9 (RLIN) | 19177 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mabhaudhi, T. |
| 9 (RLIN) | 18478 |
| 773 0# - HOST ITEM ENTRY | |
| Title | Remote Sensing |
| Related parts | v. 13, no. 20, art. 4091 |
| Place, publisher, and date of publication | Basel (Switzerland) : MDPI, 2021. |
| International Standard Serial Number | 2072-4292 |
| Record control number | 57403 |
| 856 4# - ELECTRONIC LOCATION AND ACCESS | |
| Link text | Open Access through DSpace |
| Uniform Resource Identifier | https://hdl.handle.net/10883/21718 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Article |
| Suppress in OPAC | No |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Date last seen | Price effective from | Koha item type | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Withdrawn status | Home library | Current library | Date acquired |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 10/26/2021 | 10/26/2021 | Article | Not Lost | Dewey Decimal Classification | CIMMYT Staff Publications Collection | CIMMYT Knowledge Center: John Woolston Library | CIMMYT Knowledge Center: John Woolston Library | 10/26/2021 |