Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems
| Autor: | Denis Vladimirovich Serdechnyi, Aleksey Viktorovich Osipov, Sergey Gataullin, Konstantin Vladimirovich Bublikov, Stanislav Vadimovich Suvorov, Mikhail Viktorovich Smirnov, Sergey Alekseevich Korchagin |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Video capture business.industry Computer science potato disease Flashlight Scale-invariant feature transform Conveyor belt Agriculture neural networks Convolutional neural network Support vector machine fast detection potato classification Histogram of oriented gradients machine learning Computer vision crop Artificial intelligence business Agronomy and Crop Science Algorithm defects detection |
| Zdroj: | Agronomy, Vol 11, Iss 1980, p 1980 (2021) Agronomy Volume 11 Issue 10 |
| ISSN: | 2073-4395 |
| Popis: | The article discusses the problem of detecting sick or mechanically damaged potatoes using machine learning methods. We proposed an algorithm and developed a system for the rapid detection of damaged tubers. The system can be installed on a conveyor belt in a vegetable store, and it consists of a laptop computer and an action camera, synchronized with a flashlight system. The algorithm consists of two phases. The first phase uses the Viola-Jones algorithm, applied to the filtered action camera image, so it aims to detect separate potato tubers on the conveyor belt. The second phase is the application of a method that we choose based on video capturing conditions. To isolate potatoes infected with certain types of diseases (dry rot, for example), we use the Scale Invariant Feature Transform (SIFT)—Support Vector Machine (SVM) method. In case of inconsistent or weak lighting, the histogram of oriented gradients (HOG)—Bag-of-Visual-Words (BOVW)—neural network (BPNN) method is used. Otherwise, Otsu’s threshold binarization—a convolutional neural network (CNN) method is used. The first phase’s result depends on the conveyor’s speed, the density of tubers on the conveyor, and the accuracy of the video system. With the optimal setting, the result reaches 97%. The second phase’s outcome depends on the method and varies from 80% to 97%. When evaluating the performance of the system, it was found that it allows to detect and classify up to 100 tubers in one second, which significantly exceeds the performance of most similar systems. |
| Databáze: | OpenAIRE |
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