Detection of Cassava Leaves in Multi-Temporally Acquired Digital Images of a Cassava Field Under Different Brightness Levels by Simultaneous Binarization of the Images Based on Indices of Redness/Greenness

Mallika Srisutham, Ryoichi Doi, Anan Polthanee and Masaru Mizoguchi

Modern Applied Science, 2014, vol. 8, issue 5, 87

Abstract: Plant leaf area reveals various types of abnormalities which can enable appropriate plant/crop management actions. The quantification of plant leaf area is now feasible using commonly available digital photographing tools. Changes in brightness, however, make it difficult to compare leaf areas in digital photographs acquired at multiple time points. This difficulty could be overcome by employing an index of redness/greenness (R/G), which was suggested to be one of the best indices to discriminate between plant leaves and other objects such as soils. R/G and other indices were examined when discriminating cassava leaves from other objects in a field. A surveillance camera captured digital photographs on a daily basis. Of these, 183 photographs were stored. They were pasted into a single image file and simultaneously analyzed. The International Commission on Illumination color model’s a* was the best index in the discrimination, with a distinctiveness score of 1.36. R/G was the second best, with a distinctiveness score of 0.70. The percentage of leaf-likely pixels followed sigmoidal patterns with time, resulting in great coefficients of determination of 0.981 (a*) and 0.965 (R/G). The percentage of leaf-likely pixels and cassava leaf weight had a real-time response relationship. The range of the 95% confidence limit was narrowed from -16 to +14% of a predicted value of 98% leaf-likely pixels for R/G to ±12% for a*. Thus, the simultaneous binarization and the detection of leaf-likely pixels in the photographs acquired under different brightness levels was enabled with improved discrimination accuracy by employing a*.

Date: 2014
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