László Moldvai

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 mAP@0.5 = 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.