Publications

An end-to-end workflow for detecting creeping thistle (Cirsium arvense) in UAV imagery and turning the detections into sprayer-compatible management zones. A single-class YOLO model trained on 800 annotated tiles (augmented to 13,280 images) reached [email protected] = 78.97 % and F1 = 77.2 %. Herbicide was needed on only 32.43 % of the field, a 67.57 % reduction compared with full-area spraying.

A six-phase, feature-based computer vision method that detects crop rows and generates weed pressure maps from UAV images taken at 20–30 m, without training data or deep learning. Tested on 208 images and 120 artificial datasets; the in-row concentration ratio (IRCR) tells whether a result is usable. With 678 crop plants and 600 weeds the method reached F1 = 86.32 %, and the weed-to-crop ratio correlated 98–99 % with ground truth.

A review of recent machine vision, deep learning and AI work in tomato production: fruit classification, counting and yield estimation, plant health monitoring (weeds, pests, leaf diseases, fruit disorders), vehicle navigation and harvesting robots, with a focus on the image processing algorithms of the last two years.

How few training images are enough? Traditional feature-based computer vision (shape, distance-transform, colour-histogram and texture features) with six classifiers was tested on 5–160 images per weed type in a four-class problem. With 160 images per class a 94.56 % recall was reached, close to deep-learning accuracy with a tenth of the data.

Ripe-tomato detection by HSV colour segmentation was measured under 15 artificial illumination levels. Results are stable at or above about 3,000 lx; below that the detected fruit size shrinks, and near 1,600 lx only about half of the real size is measured, so controlled lighting is critical for precise measurement.

An overview of RGB-image-based weed detection and automated weed control, and how accurate targeting of weeds lowers production costs and chemical use in precision agriculture.

(Hungarian-language article.) Technological steps of tomato yield forecasting with machine vision: which data image processing can provide in precision crop production and how they support yield, harvest-time and stress estimation.