Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation
Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of...
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MDPI AG
2021-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/4/598 |
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| author | Daniel O. Wasonga Afrane Yaw Jouko Kleemola Laura Alakukku Pirjo S.A. Mäkelä |
| author_facet | Daniel O. Wasonga Afrane Yaw Jouko Kleemola Laura Alakukku Pirjo S.A. Mäkelä |
| author_sort | Daniel O. Wasonga |
| collection | DOAJ |
| description | Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R<sup>2</sup> = 0.90), followed by leaf area (R<sup>2</sup> = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R<sup>2</sup> > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R<sup>2</sup> = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers. |
| first_indexed | 2024-03-09T05:10:32Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:10:32Z |
| publishDate | 2021-02-01 |
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| spelling | doaj.art-e8c1a1415a24404997fdb37bbbb1c7f22023-12-03T12:50:26ZengMDPI AGRemote Sensing2072-42922021-02-0113459810.3390/rs13040598Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium FertigationDaniel O. Wasonga0Afrane Yaw1Jouko Kleemola2Laura Alakukku3Pirjo S.A. Mäkelä4Department of Agricultural Sciences, University of Helsinki, P.O. Box 27, FIN-00014 Helsinki, FinlandDepartment of Agricultural Sciences, University of Helsinki, P.O. Box 27, FIN-00014 Helsinki, FinlandDepartment of Agricultural Sciences, University of Helsinki, P.O. Box 27, FIN-00014 Helsinki, FinlandDepartment of Agricultural Sciences, University of Helsinki, P.O. Box 27, FIN-00014 Helsinki, FinlandDepartment of Agricultural Sciences, University of Helsinki, P.O. Box 27, FIN-00014 Helsinki, FinlandCassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R<sup>2</sup> = 0.90), followed by leaf area (R<sup>2</sup> = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R<sup>2</sup> > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R<sup>2</sup> = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers.https://www.mdpi.com/2072-4292/13/4/598early growth<i>Manihot esculenta</i>nondestructiveregression modelsspectral indices |
| spellingShingle | Daniel O. Wasonga Afrane Yaw Jouko Kleemola Laura Alakukku Pirjo S.A. Mäkelä Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation Remote Sensing early growth <i>Manihot esculenta</i> nondestructive regression models spectral indices |
| title | Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation |
| title_full | Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation |
| title_fullStr | Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation |
| title_full_unstemmed | Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation |
| title_short | Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation |
| title_sort | red green blue and multispectral imaging as potential tools for estimating growth and nutritional performance of cassava under deficit irrigation and potassium fertigation |
| topic | early growth <i>Manihot esculenta</i> nondestructive regression models spectral indices |
| url | https://www.mdpi.com/2072-4292/13/4/598 |
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