An investigation into the potential of Gabor wavelet features for scene classification in wild blueberry fields

• Main Authors: Gashaw Ayalew, Qamar Uz Zaman, Arnold W. Schumann, David C. Percival, Young Ki Chang
• Format: Article
• Language: English
• Published: KeAi Communications Co., Ltd. 2021-01-01
• Series: Artificial Intelligence in Agriculture
• Subjects: Wavelet transforms; Wild blueberry; Computer vision; Machine learning; Discriminant analysis; Precision agriculture
• Online Access: http://www.sciencedirect.com/science/article/pii/S2589721721000155

A Gabor wavelets based technique was investigated as a potential tool for scene classification (into one of bare patch, plant, or weed) for its ultimate utility in site-specific application of agrochemicals in wild blueberry fields.Images were gathered from five sites located in central Nova Scotia, Canada. Gabor wavelet features extracted from these images were used to classify scenes according to visually determined classes using step-wise linear discriminant analysis.For individual fields, classification accuracy attained ranged between 87.9% and 98.3%; selected Gabor features ranged between 27 and 72; contextual accuracy for herbicide ranged between 67.5% and 96.7%, and contextual accuracy for fertilizer ranged between 63.6% and 97.1%. The pooled scenes yielded a classification accuracy of 81.4%, and contextual accuracy figures of 61.1% and 73.1% for herbicide and fertilizer, respectively, with selected Gabor features of 36.Calibrations based on LDA coefficients from the pooled scenes could help avoid the need to re-calibrate for each field, whereas those based on individual field LDA coefficients could improve accuracy, hence enable saving on expensive agrochemicals.