استفاده از روش بینایی استریو به‌منظور طبقه‌بندی کلوخه‌های حاصل از عملیات خاک‌ورزی

سطح یک خاک خاک‌ورزی شده مملو از خاک‌دانه‌ها و کلوخه‌هایی است که دارای اندازه‌های مختلف می‌باشند به گونه‌ای‌که اندازه آن‌ها از نظر کیفیت خاک‌ورزی و میزان مصرف انرژی در آماده‌سازی بستر بذر از اهمیت به‌سزایی برخوردار است. در این پژوهش طبقه‌بندی کلوخه‌های حاصل از خاک‌ورزی از طریق بینایی استریو بر اساس طول، عرض و ارتفاع آن‌ها مورد توجه قرار گرفته است. از بین این ابعاد، محاسبه ارتفاع (ضخامت) کلوخه‌ها همواره با چالش روبه‌رو بوده است؛ چرا که محاسبه آن که در اصطلاح پردازش تصویر به عمق تصویر معروف است، با یک تصویر تکی غیرممکن و یا بسیار سخت می‌باشد که برای این به سه‌بعدی‌سازی تصویر نیاز است. در این مطالعه یک مجموعه داده تصاویر استریو از سطح خاک در زمین زراعی تهیه گردید. تعداد این زوج تصاویر استریو برابر 312 جفت بود که برای بازسازی سه‌بعدی آن‌ها به‌ترتیب عملیات کالیبراسیون، استخراج نقاط کلیدی از زوج تصاویر استریو، تطابق نقاط یافته شده و محاسبه مدل ابر نقطه‌ای روی آن‌ها انجام پذیرفت و تصاویر به شکل سه‌بعدی درآمدند. با به‌دست آمدن تصویر سه‌بعدی ارتفاع کلوخه‌ها محاسبه شدند و نهایتاً با در اختیار داشتن این اطلاعات و نیز استفاده از مدل تحلیل تمییزی به‌عنوان یک دسته‌بند خطی، کلوخه‌ها با اندازه‌های گوناگون طبقه‌بندی شدند. بالاترین دقت طبقه‌بندی 96.2% و نیز دقت کلی طبقه‌بندی برابر 83.7% به‌دست آمد. نتایج این مطالعه نشان داد رویکرد بینایی استریو می‌تواند با تعیین ابعاد کلوخه‌ها به‌ویژه ارتفاع آن‌ها در کیفیت‌سنجی عملیات خاکورزی به‌صورت رضایت‌بخشی مورد استفاده قرار گیرد.

A Modern Approach for Classification of Soil Aggregates based on Stereo Vision

Introduction;Stereo vision is an approach to 3D information from multiple 2D views of a scene. The 3D information can be extracted from a pair image, as known stereo pair by estimating the relative depth of points in the scene.;Soil aggregate size distribution is one of the most important issues in the agriculture sector which highly affects energy consumed for preparing the field before planting. Mean weight diameter of clods is a standard metric for determining clod (big aggregates) size. Conventional methods are based on sieving soil samples to calculate the MWD. However, they are faced with several challenges in larger scales and practical applications. Furthermore, due to inherent limitations of soil environment and also being a tedious work, traditional methods would beuse to estimate the metric higher or lower than actual value.;As new methods, researchers are using computer vision techniques as virtual sieve so that the size of clods can be determined via processing digital images which have been taken from soil surface. Although, imagebased methods have solved many of previous problems, their accuracy is not so high due to the complexity of soil environment and overlapping colds, and needs to be improved. In order to overcome the mentioned challenges, in the current study stereo vision method was developed so that it is possible to extract the third dimension information as height of clods which helps us to categorize clods into their own class.;Materials and Methods;In this study, the W3Fujifilm stereo camera equipped with two 10megapixel CCD sensors for both left and right lenses, and baseline spacing of 7.5 cm was used. The distance between the camera lens and the ground was also set to 60 cm.;In order to get three components of soil clods including (x, y, z), point cloud was investigated. For this, local features were extracted using a SIFT feature detector. The SIFT algorithm is robust against scale, rotation and illumination changes, so that these specifications have made it as a strong tool in the field of stereo vision. Then, the extracted features (keypoints) were matched between two stereo pair images by means of Brute Force algorithm and the location of all corresponding points were determined and point cloud was obtained.;At the final stage, three features including length, width and height of all six classes of soil clods were entered into a linear classifier entitled discriminant analysis. This classifier as a linear separator classified these six classes based on appropriate functions in a 5 dimensional space.;Results and Discussion;Results of classification model showed that the height (thickness) of clods have more distinguishing different soil clods. The reason for this refers to the event of overlapping, because most of clods were touched each other after sieving. Consequently, the length and width of clods had not significant effect in soil aggregates classification.;In order to analysis the result of soil aggregate classification, confusion matrix was calculated and the overall classification accuracy was achieved 83.7%. The lowest and highest accuracy were obtained for class 1 (the littlest class) and class 6 (the biggest class), respectively due to their low and high height from the soil surface.;Conclusions;In this research, the basic geometrical features including length, width and height were extracted from stereo pair digital images via stereo vision techniques to classify six classes of soil clods. This aim was reached by 3D reconstruction of image data, so that the height of each image as the third component of (x,y,z) was obtained as well as the length and width. The results of classification indicated that the stereo vision technique had the satisfactory performance in determining the aggregate size distribution which is one of the most important indices for tilled soil quality.