Accelerating Cassava Genetic Improvement through NDVI-Based High-Throughput Phenotyping

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 affordable handheld sensor (Trimble GreenSeeker). 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.Genetic parameters including broad-sense heritability and genotype-by-environment interactions were estimated. Results showed moderate to high heritability for important traits such as fresh root yield (FYLD), dry matter content (DM), and harvest index (HI). NDVI data, especially at 6 months after planting, demonstrated strong predictive power (R² up to 0.9) for yield components, with prediction accuracy varying across locations. Significant negative correlations between lodging (LODG) and yield traits highlighted the influence of plant architecture on productivity in cassava. These findings affirm 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.