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Background <p>Cassava (<i>Manihot esculenta</i> Crantz) is an important food security crop in sub-Saharan Africa and other tropical regions, but its genetic improvement is hindered by long breeding cycles and labour-intensive phenotyping procedures. This study aimed to develop a rapid phenotyping protocol and assess its predictive capacity for yield and plant architecture traits in cassava using Normalized Difference Vegetation Index (NDVI) data obtained with an affordable handheld sensor (Trimble GreenSeeker).</p> Methods <p>A diverse panel of 453 cassava accessions was evaluated across two contrasting agroecological zones in Nigeria: Mokwa (Southern Guinea Savannah) and Onne (Humid Forest) during the 2021/2022 planting season. NDVI data collected at 3, 6, and 9 months after planting (MAP) were integrated with ground truth phenotypic measurements of 26 agronomic traits.</p> Results <p>Broad-sense heritability estimates were moderate to high for fresh root yield (0.56), dry matter content (0.61), starch content (0.61) and harvest index (0.66), indicating substantial potential for genetic gains through selection. Genotypic correlations revealed strong negative relationships between lodging and yield-related traits, fresh root yield (<i>r</i> = − 0.56) and harvest index (<i>r</i> = − 0.72). This highlights the importance of plant architecture to cassava productivity. NDVI measured at 6 months after planting showed high predictive accuracy for fresh root yield, dry yield and root weight in Mokwa (R² 0.90), while moderate prediction accuracy was observed in Onne (R² 0.50) due to environmental effects.</p> Conclusions <p>These findings confirm the applicability of handheld NDVI sensors as cost-effective tools for enhanced phenotyping and selection in cassava breeding programs for rapid genetic gains and varietal development under diverse field conditions.</p>

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Accelerating cassava genetic improvement through NDVI-based high-throughput phenotyping

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • Abiodun Fatai Olayinka,
  • Adesike Oladoyin Olayinka,
  • Edwige Gaby Nkouaya Mbanjo,
  • Daniel Kwadjo Dzidzienyo,
  • Samuel Kwame Offei,
  • Pangirayi Bernard Tongoona,
  • Chiedozie Egesi,
  • Ismail Yusuf Rabbi

摘要

Background

Cassava (Manihot esculenta Crantz) is an important food security crop in sub-Saharan Africa and other tropical regions, but its genetic improvement is hindered by long breeding cycles and labour-intensive phenotyping procedures. This study aimed to develop a rapid phenotyping protocol and assess its predictive capacity for yield and plant architecture traits in cassava using Normalized Difference Vegetation Index (NDVI) data obtained with an affordable handheld sensor (Trimble GreenSeeker).

Methods

A diverse panel of 453 cassava accessions was evaluated across two contrasting agroecological zones in Nigeria: Mokwa (Southern Guinea Savannah) and Onne (Humid Forest) during the 2021/2022 planting season. NDVI data collected at 3, 6, and 9 months after planting (MAP) were integrated with ground truth phenotypic measurements of 26 agronomic traits.

Results

Broad-sense heritability estimates were moderate to high for fresh root yield (0.56), dry matter content (0.61), starch content (0.61) and harvest index (0.66), indicating substantial potential for genetic gains through selection. Genotypic correlations revealed strong negative relationships between lodging and yield-related traits, fresh root yield (r = − 0.56) and harvest index (r = − 0.72). This highlights the importance of plant architecture to cassava productivity. NDVI measured at 6 months after planting showed high predictive accuracy for fresh root yield, dry yield and root weight in Mokwa (R² 0.90), while moderate prediction accuracy was observed in Onne (R² 0.50) due to environmental effects.

Conclusions

These findings confirm the applicability of handheld NDVI sensors as cost-effective tools for enhanced phenotyping and selection in cassava breeding programs for rapid genetic gains and varietal development under diverse field conditions.