U.S. Department of Agriculture: Agricultural Research Service, Lincoln, Nebraska


Date of this Version




doi: 10.1016/j.inpa.2019.01.005


This is an open access article under the CC BY-NC-ND license.

Copyright (c)2019 China Agricultural University.


The pinto bean is one of widely consumed legume crop that constitutes over 42% of the U.S dry bean production. However, limited studies have been conducted in past to assess its quantitative and qualitative yield potentials. Emerging remote sensing technologies can help in such assessment. Therefore, this study evaluates the role of ground-based multispectral imagery derived vegetation indices (VIs) for irrigated the pinto bean stress and yield assessments. Studied were eight cultivars of the pinto bean grown under conventional and strip tillage treatments and irrigated at 52% and 100% of required evapotranspiration. Imagery data was acquired using a five-band multispectral imager at early, mid and late growth stages. Commonly used 25 broadband VIs were derived to capture crop stress traits and yield potential. Principal component analysis and Spearman’s rank correlation tests were conducted to identify key VIs and their correlation (rs) with abiotic stress at each growth stage. Transformed difference vegetation index, nonlinear vegetation index (NLI), modified NLI and infrared percentage vegetation index (IPVI) were consistent in accounting the stress response and crop yield at all growth stages (rs > 0.60, coefficient of determination (R2): 0.50–0.56, P < 0.05). Ten other VIs significantly accounted for crop stress at early and late stages. Overall, identified key VIs may be helpful to growers for precise crop management decision making and breeders for crop stress response and yield assessments.