Citation: Kassim, Y.B.; Oteng-Frimpong, R.; Puozaa, D.K.; Sie, E.K.; Abdul Rasheed, M.; Abdul Rashid, I.; Danquah, A.; Akogo, D.A.; Rhoads, J.; Hoisington, D.; et al. High-Throughput Plant Phenotyping (HTPP) in Resource-Constrained Research Programs: A Working Example in Ghana. Agronomy 2022, 12, 2733. https://doi.org/10.3390/ agronomy12112733 Academic Editor: Christina Eynck Received: 9 October 2022 Accepted: 2 November 2022 Published: 4 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). agronomy Article High-Throughput Plant Phenotyping (HTPP) in Resource-Constrained Research Programs: A Working Example in Ghana Yussif Baba Kassim 1 , Richard Oteng-Frimpong 1, * , Doris Kanvenaa Puozaa 1 , Emmanuel Kofi Sie 1 , Masawudu Abdul Rasheed 1 , Issah Abdul Rashid 1 , Agyemang Danquah 2 , Darlington A. Akogo 3 , James Rhoads 4 , David Hoisington 4 , Mark D. Burow 5,6 and Maria Balota 7 1 CSIR-Savanna Agricultural Research Institute, Tamale P.O. Box TL 52, Ghana 2 West Africa Centre for Crop Improvement (WACCI), University of Ghana, Legon, Accra P.O. Box LG 1181, Ghana 3 KaraAgro AI, Accra P.O. Box LG 172, Ghana 4 Feed the Future Innovation Lab for Peanut, University of Georgia, Athens, GA 30602, USA 5 Texas A & M AgriLife Research, Lubbock, TX 79403, USA 6 Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA 7 School of Plant and Environmental Sciences, Virginia Tech, Tidewater Agricultural Research and Extension Center, Suffolk, VA 23437, USA * Correspondence: kotengfrimpong@gmail.com Abstract: In this paper, we present a procedure for implementing field-based high-throughput plant phenotyping (HTPP) that can be used in resource-constrained research programs. The procedure relies on opensource tools with the only expensive item being one-off purchase of a drone. It includes acquiring images of the field of interest, stitching the images to get the entire field in one image, calculating and extracting the vegetation indices of the individual plots, and analyzing the extracted indices according to the experimental design. Two populations of groundnut genotypes with different maturities were evaluated for their reaction to early and late leaf spot (ELS, LLS) diseases under field conditions in 2020 and 2021. Each population was made up of 12 genotypes in 2020 and 18 genotypes in 2021. Evaluation of the genotypes was done in four locations in each year. We observed a strong correlation between the vegetation indices and the area under the disease progress curve (AUDPC) for ELS and LLS. However, the strength and direction of the correlation depended upon the time of disease onset, level of tolerance among the genotypes and the physiological traits the vegetation indices were associated with. In 2020, when the disease was observed to have set in late in medium duration population, at the beginning of the seed stage (R5), normalized green-red difference index (NGRDI) and variable atmospheric resistance index (VARI) derived at the beginning pod stage (R3) had a positive relationship with the AUDPC for ELS, and LLS. On the other hand, NGRDI and VARI derived from images taken at R5, and physiological maturity (R7) had negative relationships with AUDPC for ELS, and LLS. In 2021, when the disease was observed to have set in early (at R3) also in medium duration population, a negative relationship was observed between NGRDI and VARI and AUDPC for ELS and LLS, respectively. We found consistently negative relationships of NGRDI and VARI with AUDPC for ELS and LLS, respectively, within the short duration population in both years. Canopy cover (CaC), green area (GA), and greener area (GGA) only showed negative relationships with AUDPC for ELS and LLS when the disease caused yellowing and defoliation. The rankings of some genotypes changed for NGRDI, VARI, CaC, GA, GGA, and crop senescence index (CSI) when lesions caused by the infections of ELS and LLS became severe, although that did not affect groupings of genotypes when analyzed with principal component analysis. Notwithstanding, genotypes that consistently performed well across various reproductive stages with respect to the vegetation indices constituted the top performers when ELS, LLS, haulm, and pod yields were jointly considered. Keywords: field-based high-throughput plant phenotyping; resource-constrained; groundnut; early leaf spot; late leaf spot; canopy cover; green area (GA); greener area (GGA); normalized green-red difference index (NGRDI); crop senescence index (CSI) Agronomy 2022, 12, 2733. https://doi.org/10.3390/agronomy12112733 https://www.mdpi.com/journal/agronomy