A Computer Vision-Based Approach to Estimate Disease Severity for Field-Taken Wild Blueberry Images

Background and Objective: In contrast with the laboratory environment, it is challenging to quickly and accurately rate disease severity in real farming conditions for high-density crops like blueberries. In field-taken images, the target diseased organs are usually shaded, interfered, occluded and backgrounded by other plants or plant parts, which are often irrelevant to severity estimation. This study aimed to develop and validate a computer vision-based severity estimation algorithm for mummy berry disease, which enables labor-free severity estimation with high accuracy and applicability in real farming conditions. Materials and Methods: This study developed a fast and accurate severity estimation algorithm for wild blueberry diseases by utilizing computer vision-based techniques. Firstly, this study employed a novel deblurring process using defocus estimation to effectively remove blurred parts so that the diseased and healthy target organs can be separated from the irrelevant background. This method was also enhanced by using adjustable parameter settings so that low-quality images such as those without clear focus could be properly handled. Secondly, by converting RGB features into HSV space followed by bootstrap forest modeling, diseased organs can be automatically segmented and then the severity can be estimated by calculating the ratio of total diseased pixels to the total pixels excluding background. Results: This step can effectively alleviate the negative impact of light variations such as shading on diseased organs. Verifications and experiments conducted on 400 disease images demonstrated that this approach can effectively identify diseased and healthy plant organs and make an accurate estimation with less than an average 5% relative error across different levels of background complexity and image quality. Conclusion: The method can serve as an auto-labeling tool to automatically rate the disease severity for field-taken images, on which severity estimation deep learning models can be trained without the limitation of data scarcity.