Abstract
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.
Highlights
The automatic selection of vegetables, root crops, and fruits at the time of laying them for storage and during the preparation of seed material using computer vision can reduce the volume of pesticides applied and storage costs
For example, based on the conclusions of modern researchers, convolutional networks that are most promising for solving computer vision problems are not able to process the stream of images from a video camera of rapidly moving objects of small size and a significant number [32]
This method was created for recognizing human faces and did not give good results when used to detect potato tubers; by selecting preprocessing filters, we achieved a probability of 97%, which corresponds to the results of a convolutional neural network [25,26,27,28,29,30,31]
Summary
To increase productivity in agriculture, farmers use pesticides widely, which negatively affect the human body. Chemical pesticides are gradually being replaced by pesticides based on bacteria, fungi, and viruses [1]. This process is hampered, both at the level of legislation of some states and the level of farms [2]. In this situation, the automatic selection of vegetables, root crops, and fruits at the time of laying them for storage and during the preparation of seed material using computer vision can reduce the volume of pesticides applied and storage costs
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