Study on distribution map of weeds in rice field based on UAV remote sensing

Objective  To obtain and analyze the low altitude remote sensing image of rice field, acquire the weed distribution map, and provide a reference for the precious pesticide application of weeds in the field.

Method  Three machine learning algorithms including support vector machine (SVM), K-nearest neighbor (KNN) and AdaBoost were used to classify and compare the weed visible light images in rice field captured by UAV after color feature extraction and principal component analysis (PCA) dimensionality reduction. A convolutional neural network (CNN) which can automatically obtain the image features without feature extraction and dimensionality reduction was introduced to classify the weed images and improve the classification accuracy.

Result  The run time of test set based on SVM, KNN and AdaBoost were 0.500 4, 2.209 2 and 0.411 1 s, and the classification accuracies were 89.75%, 85.58% and 90.25% respectively; The classification accuracy of image based on CNN was 92.41%, which was higher than those of three machine learning algorithms. All machine learning algorithms and CNN could effectively recognize rice and weed, acquire weed distribution information, and generate distribution map of weed in rice field.

Conclusion  The classification accuracy of weed in rice field based on CNN is the highest, and the weed distribution map generated by CNN is the best.