Forestry

Agriculture

Engineering

Ecology

Pure mathematics

Biology

Automation

Field (mathematics)

Agricultural engineering

Computer science

Geology

Mechanical engineering

Crop

Precision agriculture

Database

Pattern recognition (psychology)

Mathematics

Geography

Image (mathematics)

Hough transform

Row crop

Agronomy

Environmental science

Artificial intelligence

Remote sensing

Deep learning

Artificial neural network

Convolutional neural network

Computer vision

IEEE Access

Nowadays, the development of robots and smart tractors for the automation of sowing, harvesting, weeding etc. is transforming agriculture. Farmers are moving from an agriculture where everything is applied uniformly to a much more targeted farming. This new kind of farming is commonly referred to as precision agriculture. However for autonomous guidance of these agricultural machines and even sometimes for weed detection an accurate detection of crop rows is required. In this paper we propose a new method called CRowNet which uses a convolutional neural network (CNN) and the Hough transform to detect crop rows in images taken by an unmanned aerial vehicle (UAV). The method consists of a model formed with SegNet (S-SegNet) and a CNN based Hough transform (HoughCNet). The performance of the proposed method was quantitatively compared to traditional approaches and it showed the best and most robust result. A good crop row detection rate of 93.58% was obtained with an IoU score per crop row above 70%. Moreover the model trained on a given crop field is able to detect rows in images of different types of crops.

CRowNet: Deep Network for Crop Row Detection in UAV Images