Canopy light interception (LI) is a determining factor for crop growth and yield. Crop yield depends on a canopy's capacity to intercept incident solar radiation, which in turn depends on the available leaf area, its structure, and its efficiency in converting the energy captured by the plant into biomass. Digital images offer a series of advantages over other methods of LI estimation, including the possibility to directly process images by computer for which free software is available. The objectives of this work were to develop a simple, economical method for determining LI in low-lying crops such as processing tomato using digital images obtained with a standard, commercial camera and free software and to evaluate the influence of different types of soil coverage (bare soil and plastic mulch) on LI. Photographs of the selected areas were taken using a digital camera at a distance of 160 cm above the center of each area. The resulting digital images were then analyzed with the free software GIMP 2.2 and IMAGE J. Three methods [area (SA), contour (SC). and reclassification (SR)] were used to quantify the percentage of groundcover (PGC). They were applied to the same images and compared with LI as measured with a line quantum sensor at solar noon. There was a close relationship between LI and estimated PGC with all three methods and for different soil cover regimes. In all cases, there was a linear adjustment with a significant correlation coefficient (P < 0.01) and an r2 of greater than 0.88. The adjustment with RI was narrowest when the SR method was used to estimate PGC (r2 = 0.93) followed by SC (r2 = 0.92) and SA (r2 = 0.88). Measurements of LI based on digital images offered practical advantages with respect to the use of photosynthetically active radiation bars because the latter must be used at solar noon. In contrast, measurements obtained with a digital camera can be taken at any time of day and bright sunshine is not necessary. Different correlations were obtained for bare soil and plastic mulch conditions, so it was necessary to use a different equation to estimate LI under each condition.
One estimate of a crop's ability to capture light energy is the leaf area index (LAI), which is defined as the proportion of leaf area per unit of land area. Direct methods of estimation involve determining the LAI in a significant area of cultivation and individually measuring the leaf surface, which is often tedious. The objective of this study was to develop a cheap and simple method for determining LAI based on the percentage of groundcover (PGC) measured in two vegetable crops with notable differences in leaf type and plant architecture using digital images obtained with a commercial camera and applying open-source software. The PGC values obtained from digital image analysis in cauliflower and tomato crops and the measurements of LAI obtained by destructive sampling (measured with a planimeter) allowed us to obtain a relationship between two variables (r2 > 0.88). In all cases, the extinction coefficients were obtained from comparisons of LAI and PGC with values ranging between 0.75 and 0.85 for processing tomato and 0.60 and 0.70 for cauliflower. The method used allows non-destructive estimations of LAI that are comparable with other more expensive indirect methods that require a skilled operator.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.