Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning

Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning

Blog Article

Abstract Background Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield nobivac 1-hcp and causes heavy economic loss.Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding.Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipments.These methods are time-consuming, inefficient and laborious.In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of the experts.

Methods To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning and unmanned aerial vehicle (UAV) images captured by a consumer UAV.We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning.The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of crop candidates.To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials captured by the DJI Phantom 4 Pro V2.0.

Then, five classic deep learning networks (AlexNet, maglione loro piana VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition.Result and conclusion The accuracy of the five deep learning networks used in our work is all over 92%.Especially, ResNet50 provides the best accuracy (93.33 $$%$$ % ) in this task.In addition, we also compare deep learning networks with traditional machine learning methods.

The comparison results show that the deep learning-based methods significantly outperform the traditional machine learning-based methods in our task.The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.