Validation of an Operator-Assisted Module to Measure Weed and Crop Leaf Cover by Digital Image Analysis

Abstract

The practical application of yield loss prediction models using relative leaf area of weeds is limited due to the lack of a quick and accurate method of leaf area estimation. Leaf cover (the vertical projection of plant canopy on the ground) can be used to approximate leaf area at early stages of plant development. An automated digital image analysis system for measuring leaf cover has been developed. The system has an operator-assisted module aimed at validating the automated functions. The objective of this research was to demonstrate the accuracy of the operator-assisted module under different weed–crop conditions. A laboratory experiment was conducted using simulated weed–crop populations. Two additional field experiments were conducted using corn in competition with: (1) common lambsquarters, barnyardgrass, or a mixture of both species, and (2) a natural weed community. In the laboratory experiment, a narrow linear relation was observed between leaf cover estimated with the operator-assisted module and leaf area measured with an optical area meter (r2> 0.98). In field experiments, the regression between corn leaf cover estimated by the operator-assisted module and corn leaf area measured with the optical area meter was not as good (r2< 0.55). The poor performance of the module was probably due to the overlapping and the architecture of corn leaves (especially unexpanded leaves). Nevertheless, the system showed high precision in estimating leaf area of both grassy weeds and broadleaf weeds (r2> 0.89). Generally, the accuracy of the estimates decreased as the growth stage became more advanced. Apart from its initial purpose as a calibration tool for the automated system, the operator-assisted module can have several potential research applications. It can be used: (1) as an alternative to destructive leaf area measurement at early stages of plant development, (2) as a tool in the study of plant competitive ability, and (3) as an objective and quantitative support to visual observations.

Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

“…The quality of information provided by digital photographs is dependent on the resolution of the images. In the present study, the resolution was approximately 0.048 mm 2 per pixel, which is more accurate than that reported by Ngouajio et al (1998) and Rasmussen et al (2007), which were 1.0 and 0.25 mm 2 per pixel, respectively. However, the main purpose of using digital photographs in our study was not as a means of decision making for a particular weed control method.…”

Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature

“…Point sampling does not appear to be reliable for large areas such as corn fields. However, it is possible to have a reliable estimate of the weed infestation with less than 0.06% of the surface sensed (Ngouajio et al 1998). Therefore, a series of fluoro-sensors could be aligned in front of a tractor and sense the vegetation at a relatively high frequency, triggering the herbicide spray if necessary.…”

Métodos de quantificação da cobertura foliar da infestação de plantas daninhas e da cultura da soja

“…A principal restrição à avaliação visual consiste na subjetividade do avaliador (NGOUAJIO et al, 1998;NGOUAJIO et al, 1999). A experiência do observador ou características da espécie (cor, forma de crescimento) podem influenciar consideravelmente os dados (LOTZ et al, 1994 …”