Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network

Detection of immature peach fruits would help growers to create yield maps which are very useful tools for adjusting management practices during the fruit maturing stages. Machine vision algorithms were developed to detect and count immature peach fruit in natural canopies using colour images. This study was the first effort to detect immature peach fruit in natural environment to the authors' knowledge. Captured images had various illumination conditions due to both direct sunlight and diffusive light conditions that make the fruit detection task more difficult. A training set and a validation set were used to develop and to test the algorithms. Different image scanning methods including finding potential fruit regions were developed and used to parse fruit objects in the natural canopy image. Circular Gabor texture analysis and 'eigenfruit' approach (inspired by the 'eigenface' face detection and recognition method) were used for feature extraction. Statistical classifiers, a neural network and a support vector machine classifier were built and used for detecting peach fruit. A blob analysis was performed to merge multiple detections for the same peach fruit. Performance of the classifiers and image scanning methods were introduced and evaluated. Using the proposed algorithms, 84.6, 77.9 and 71.2 % of the actual fruits were successfully detected using three different image scanning methods for the validation set.