Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

Abstract

Detecting sugar beetroot crops with mechanical damage using machine learning methods is necessary for fine-tuning beet harvester units. The Agrifac HEXX TRAXX harvester with an installed computer vision system was investigated. A video camera (24 fps) was installed above the turbine, which receives the dug-out beets after the digger and is connected to a single-board computer. At the preprocessing stage, static and insignificant image details were revealed. Canny edge detector and excess green minus excess red (ExGR) method were used. The identified areas were excluded from the image. The remaining areas were glued with similar areas of another image. As a result, the number of images entering the second stage of preprocessing was reduced by half. Then Otsu's binarization was used. The main stage of image processing is divided into two sub-stages: detection and classification. The improved YOLOv4tiny method was chosen for root crop detection using a single-board computer (SBC). This method allows processing up to 14 images of 416 × 416 pixels with 86% precision and 91% recall. To classify root crop damage, we considered two algorithms as candidates: 1. bag of visual words (BoVW) with a support vector machine (SVM) classifier using histogram of oriented gradients (HOG) and scale-invariant feature transform (SIFT) descriptors; 2. convolutional neural networks (CNN). Under normal lighting conditions, CNN showed the best accuracy, which ranged from 97% to 100%, depending on the damage class. The implemented methods were used to detect and classify blurred images of sugar beetroots, which were previously rejected. For improved YOLOv4-tiny precision was 74% and recall was 70%. CNN classification accuracy ranged from 90% to 95% depending on the root crops damage class.

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People

“…Computer vision is a field of computer science that deals with how computers can gain high-level understanding from digital images or videos. Efficient computer vision techniques can run on single board computers, which enables them to be used in several applications such as traffic sign recognition [ 18 ], video surveillance [ 19 ], obstacle recognition [ 20 ], smart waste management [ 21 ], mechanical damage identification and classification [ 22 ] and energy saving [ 23 ]. Several computer vision–based systems have been proposed in the literature to assist visually impaired individuals in their navigation.…”

Theoretical Study of the Motion of a Cut Sugar Beet Tops Particle along the Inner Surface of the Conveying and Unloading System of a Topping Machine

“…Efficient and high-quality harvesting, using specialized machines such as multi-row self-propelled harvesters, is mandatory [11][12][13][14]. Nowadays, some modern harvesters have the possibility of installing adaptive devices based on computer vision systems, by which a significant amount of losses during the harvesting process can be reduced, thanks to, for instance, a digital two-dimensional recording system combined with a convolutional neural network (CNN) for detecting defects in harvested sugar beets [15,16].…”

Optimizing Face Recognition Inference with a Collaborative Edge–Cloud Network

“…The applications of CV proved to be a great problem solver in real-time applications. Several CV application studies have been carried out, such as detecting a compact fluorescence lamp [ 1 ], recognizing speed limit traffic signs using a shape-based approach [ 2 ], detecting sugar beetroot crops with mechanical damage [ 3 ], designing of an autonomous underwater vehicle that performs computer-vision-driven intervention tasks [ 4 ], tracking of ball movement in a smart goalkeeper prototype [ 5 ], and recognizing an obstacle on a powered prosthetic leg [ 6 ]. These papers all utilized a single-board computer for real-time deployment.…”