Performance of ANN and AlexNet for weed detection using UAV-based images

Unmanned Aerial Vehicles (UAVs) have become an integral part of several real-world applications. Their combination with other evolving paradigms such as image recognition using machine learning or deep learning algorithms has contributed to the suitability for use in smart agriculture and weed detection applications. In this paper, the performances of the Artificial Neural Network (ANN) and AlexNet algorithms for weed detection using UAV-based images have been studied. An image dataset containing 15336 segments with the following breakdown: 3249 of soil, 7376 of soybean, 3520 grass and 1191 of broadleaf weeds has been used. The partitioning for train and test sets has been done in the ratio of 70:30. Simulation results show that the conventional ANN algorithm provide an accuracy of 48.09% while the AlexNet algorithms gives an accuracy 99.8% on the test dataset.