Image processing methods to evaluate tomato and zucchini damage in post-harvest stages

Through the supply chain, the quality or quality change of the products can generate important losses. The quality control in some steps is made manually that supposes a high level of subjectivity, controlling the quality and its evolution using automatic systems can suppose a reduction of the losses. Testing some automatic image analysis techniques in the case of tomatoes and zucchini is the main objective of this study. Two steps in the supply chain are considered, the feeding of the raw products into the handling chain (because low quality generates a reduction of the chain productivity) and the cool storage of the processed products (as the value at the market is reduced). It was proposed to analyze the incoming products at the head the processing line using CCD cameras to detect low quality and/or dirty products (corresponding to specific farmers/suppliers, it should be asked to improve to maintain the productivity of the line). The second stage is analyzing the evolution of the products along the cool chain (storage and transport), the use of an App developed to be use under Android was proposed to substitute the “visual” evaluation used in practice. The algorithms used, including stages of pre-treatment, segmentation, analysis and presentation of the results take account of the short time available and the limited capacity of the batteries. High performance techniques were applied to the homography stage to discard some of the images, resulting in better performance. Also threads and renderscript kernels were created to parallelize the methods used on the resulting images being able to inspect faster the products. The proposed method achieves success rates comparable to, and improving, the expert inspection. Keywords: image processing, color space, smartphone, efficient stitching, homography, controlled supervision, artificial vision, embedded parallel processing, injury assessment, traceability, post-harvest control, feature detection DOI: 10.25165/j.ijabe.20171005.3087 Citation: Alvarez-Bermejo J A, Giagnocavo C, Li M, Morales C E, Santos D P M, Yang X T. Image processing methods to evaluate tomato and zucchini damage in post-harvest stages. Int J Agric & Biol Eng, 2017; 10(5): 126–133.