TY - GEN
T1 - Real-time UAV weed scout for selective weed control by adaptive robust control and machine learning algorithm
AU - Li, Liujun
AU - Fan, Youheng
AU - Huang, Xiaoyun
AU - Tian, Lei
N1 - Funding Information:
The research is sponsored by NSF Award Search: Award#1520588 - SBIR Phase I: Aerial Weed Scout with Robust Adaptive Control for Site-Specific Weed Control.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Aerial weed scout
KW - Convention neural network(CNN)
KW - Deep learning
KW - Image processing
KW - Machine vision
KW - Unmanned aerial vehicle(UAV)
UR - http://www.scopus.com/inward/record.url?scp=85009110131&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009110131&partnerID=8YFLogxK
U2 - 10.13031/aim.20162462667
DO - 10.13031/aim.20162462667
M3 - Conference contribution
AN - SCOPUS:85009110131
T3 - 2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016
BT - 2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016
PB - American Society of Agricultural and Biological Engineers
T2 - 2016 ASABE Annual International Meeting
Y2 - 17 July 2016 through 20 July 2016
ER -