Real-time UAV weed scout for selective weed control by adaptive robust control and machine learning algorithm

Liujun Li, Youheng Fan, Xiaoyun Huang, Lei Tian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Typically, herbicides are applied uniformly to the entire field rather than be applied to the critical areas where they're most needed. And existing weed identification approaches, such as manual visual scouting and ground-based proximal sensing, are labor intensive, time consuming, and do not scale easily to large areas. To facilitate more efficient farming, new technologies must be developed that can achieve optimal weed management and increase the crop yield by a magnitude. To date, rapid advances in UAV technology has enabled sufficient lifting capacity and endurance to support high resolution imagery over large areas. With the FAA planning to integrate UAVs into the national airspace (NAS), UAV-based aerial weed scouting is now possible to bring promising significant benefits for farmers. An unmanned aerial vehicle based aerial weed scout with machine vision system was proposed and developed for the weed identification with the sensing capability from far above the field to close to the canopy, and measuring the plant/weed density (weed infestation rate)/weed species. The canopy was segmented from the background by greenness selection and thresholding. The crop row and its centroid line was calculated and masked by its pixel density. The anomalous weed patches between the crop rows was identified and its population was mapped. For species identification, a machine learning approach is used. Convolutional Neural Network (CNN) as one of deep learning method has the advantage of less neuron weight, easy for training and good robust performance in displacement, scale and deformation, which is promising for the weed specifies classification and probability assessment. The previous weed distribution mapping and individual weed extraction helps to generate the training data and highlights their own features, which could ensure the weed identification classifier robustness and efficiency. The preliminary result shows the specific weed type could be classified probably. The results show that the proposed approach will provide an efficient and promising tool for farmers' weed identification and the resulting infestation mapping will enable a better weed control recommendation.

Original languageEnglish (US)
Title of host publication2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016
PublisherAmerican Society of Agricultural and Biological Engineers
ISBN (Electronic)9781510828759
DOIs
StatePublished - Jan 1 2016
Event2016 ASABE Annual International Meeting - Orlando, United States
Duration: Jul 17 2016Jul 20 2016

Publication series

Name2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016

Other

Other2016 ASABE Annual International Meeting
CountryUnited States
CityOrlando
Period7/17/167/20/16

Keywords

  • Aerial weed scout
  • Convention neural network(CNN)
  • Deep learning
  • Image processing
  • Machine vision
  • Unmanned aerial vehicle(UAV)

ASJC Scopus subject areas

  • Bioengineering
  • Agronomy and Crop Science

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