Remote sensing may provide a more precise method to estimate the impacts of foliar pathogens on alfalfa (Medicago sativa L.) yield, green leaf area index (GLAI), and leaf‐to‐stem ratio compared with the traditional visual estimation methods such as percentage defoliation. To test this hypothesis, field experiments were conducted at Ames, IA (Nicollet loam soil), and at Nashua, IA, in a Readlyn loam soil in 1998 and 1999. To quantify and compare the relationships between percentage defoliation, dry weight, percentage reflectance, leaf‐to‐stem ratio, and GLAI in alfalfa, a range of disease levels caused by leaf spot pathogens was achieved by varying fungicide efficacy and application frequency. The percentage of sunlight reflected from alfalfa canopies was measured each week using a handheld multispectral radiometer. Percentage defoliation, dry weight, and GLAI were also measured each week. Leaf‐to‐stem ratio was calculated as the dry weight of leaves divided by the dry weight of alfalfa stems. Using single‐point regression models, percentage reflectance explained (on average) 15, 3, 18, and 4% more of the variation in dry weight, GLAI for primary leaves (PGLAI), GLAI for secondary leaves (SGLAI), and GLAI of alfalfa, respectively, than regression models based upon percentage defoliation. Area under the curve (AUC) regression models based on percentage reflectance explained 28, 19, 23, and 32% more of the variation in dry weight, PGLAI, SGLAI, and GLAI, respectively, than models based upon percentage defoliation. This study conclusively demonstrated that percentage reflectance had a better relationship with dry weight, PGLAI, SGLAI, and GLAI than destructive and more labor‐intensive visual estimation of percentage defoliation.