Abstract
Weed emergence models have the potential to be important tools for automating weed control actions; however, producing the necessary data (e.g., seedling counts) is time consuming and tedious. If similar weed emergence models could be created by deriving emergence data from images rather than physical counts, the amount of generated data could be increased to create more robust models. In this research, repeat RGB images taken throughout the emergence period of Raphanus raphanistrum L. and Senna obtusifolia (L.) Irwin and Barneby underwent pixel-based spectral classification. Relative cumulative pixels generated by the weed of interest over time were used to model emergence patterns. The models that were derived from cumulative pixel data were validated with the relative emergence of true seedling counts. The cumulative pixel model for R. raphanistrum and S. obtusifolia accounted for 92% of the variation in relative emergence of true counts. The results demonstrate that a simple image analysis approach based on time-dependent changes in weed cover can be used to generate weed emergence predictive models equivalent to those produced based on seedling counts. This process will help researchers working on weed emergence models, providing a new low-cost and technologically simple tool for data collection.
Highlights
Identifying weed emergence patterns and germination requirements are important steps for understanding weed biology and for timely control [1,2]
Three different image analysis workflows of increasing processing intensity were compared using a subset of the R. raphanistrum emergence data to find a suitable method for the larger dataset (Table 1)
The present study shows that even in the absence of seedling counts, seedling emergence patterns can be properly described by tracking changes in pixels associated with the weed of interest over time
Summary
Identifying weed emergence patterns and germination requirements are important steps for understanding weed biology and for timely control [1,2]. Predicting weed emergence will facilitate more efficient weed management practices, such as improving the timing of weed scouting and implementation of control measures before weeds are too large and the risk of escapes increases [3]. Because of the time and specialization in weed identification needed to take these measurements, weed emergence data has been limited to small areas and few locations [4]. Interest in remote sensing and its utility for weed management has existed for some time [7,8], but recently the availability and affordability of Plants 2020, 9, 635; doi:10.3390/plants9050635 www.mdpi.com/journal/plants
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