Artificial Intelligence, specifically deep learning, is a fast-growing research field today. One of its various applications is object recognition, making use of computer vision. The combination of these two technologies leads to the purpose of this thesis. In this proj...

Weed control within continuous no-tillage agricultural cropping area is becoming more and more difficult. A major problem is that some of more prolific weeds are becoming resistant to the regular herbicides that are used and therefore more powerful and more expensive op...

Unmanned aerial systems (UAS) are able to deliver images of agricultural fields of high spatial and temporal resolution. It is, however, not trivial to extract quantitative information about weed infestations from images. This study contributes to weed research by using...

Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricu...

and the achieved weed detection accuracies are 96% using RF, 94% using SVM and 63% using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.

<jats:p>Conventional methods of uniformly spraying fields to combat weeds, requires large herbicide inputs at significant cost with impacts on the environment. More focused weed control methods such as site-specific weed management (SSWM) have become popular but require...