Identifying blueberry fruit of different growth stages using natural outdoor color images

• Natural outdoor color images were used to explore the potential of blueberry yield mapping.
• ‘Color component analysis based detection (CCAD)’ method was proposed.
• Red, blue, and hue components were selected by using FFSA method to optimally use color image.
• ‘Supervised K-means (SK-means)’ classifier method was proposed and tested.
• K-nearest neighbor classifier performed best among three classifiers tested.

This study was conducted to identify blueberry fruit of different growth stages using natural outdoor images toward the development of a blueberry yield mapping system. As blueberries usually contain different maturity stages in a same branch, identification of blueberry fruit and their maturity stages from different background is very important for yield mapping. In this study, maturity stages of the fruit were divided into four categories: mature (m), near-mature (nm), near-young (ny) and young (y). A stepwised algorithm, termed ‘color component analysis based detection (CCAD)’ method, was developed and validated to identify blueberry fruit using outdoor color images. Firstly, a dataset was built using manually cropped pixels from training images. Three color components, red (R), blue (B) and hue (H), were selected using the forward feature selection algorithm (FFSA), and used to separate all fruit of four maturity stages from background through different classifiers. In this work, not only the traditional classifiers such as K-nearest neighbor (KNN), and naïve Bayesian classification (NBC) were used, but another newly introduced ‘supervised K-means clustering classifier (SK-means)’ was also developed and applied to the dataset. In the second step, classifiers were built to separate a group of ‘mature & near-mature’ fruit from a group of ‘near-young & young’ fruit from all fruit pixels. Finally, classifiers were developed to separate mature fruit from near-mature fruit, and near-young fruit from young fruit. The classifiers obtained from these different steps were then applied to validation images, resulting in final identification. Cross validation was conducted using these different classifiers and their results were compared. KNN classifier yielded the highest classification accuracy (85–98%) from the validation set of the prebuilt pixel dataset collected from the training images in all separations. An one-way ANOVA was used to compare the performance of the three classifies, which shows KNN performed significantly better than other methods. The newly proposed ‘SK-means’ classifier yielded a fairly high accuracy (90%) for the separation of mature and near-mature fruit. The newly developed ‘CCAD’ method for blueberry was proved to be efficient for identifying blueberry fruit of different growth stages using natural outdoor color images toward the development of a blueberry yield mapping system.