Minimax-Based High Throughput Plant Phenotyping Analysis in Complex Wavelet Domain Using Adaptive Feature Extraction Algorithm
Keywords:
Plant phenotyping, high throughput, DTCWT, plant traits estimation, yield estimation and quantification.Abstract
High throughput plant phenotyping that can identify plant traits and estimate yield is an important image processing requirement. Existing techniques capture plant components or objects and characterize the objects. Processing the plant data in the spatial domain is time consuming and has to address issues such as temporal variation, overlapping and the emergence of new objects. Image analysis in the wavelet domain has demonstrated advantages in extracting features, considering edge information. In order to improvise the feature detection process in the wavelet domain with additional orientations of edge features and to address shift variance in input data, a novel method is proposed in this work based on the Dual Tree Complex Wavelet Transform (DTCWT). In the proposed method, an adaptive algorithm is developed to accurately and adequately extract the features for processing plant phenotyping images. From the results obtained for metrics such as histogram, entropy and MSE, it is observed that there is an improvement in features by over 24% as compared with the conventional method and the adaptive algorithm is adjustable to meet the set requirements. The algorithm proposed in this work can enable plant phenotyping image analysis.
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