Research Progress on Convolutional Neural Networks and Their Application in Weed Management of Field
Weed is one of the main biotic factor that affects crop yield,which can hinder crop growth and subsequently affect yield and quality of crop.Traditional methods of manual weeding,mechanical weeding and chemical weeding can no longer meet the needs of precision weeding.In recent years,deep learning technology based on convolutional neural networks(CNN)has developed rapidly and has become an important tool for image recognition,whose significant achievements have been made in agricultural fields,such as weed detection,pest identification,plant or fruit counting and fruit maturity grading.In order to make weed management of field more efficient and promote the intelligence of agricultural production,based on the research status of CNN,the research progress of CNN in field weed management from the aspects of object detection,image segmentation,image classification,image applications on basis of unmanned aerial vehicle(UAV)were summarized,and prospects were made from three aspects of data collection,accuracy of weed detection and generalization ability of the mode.In summary,research on weed management applications based on CNN has achieved certain results,but there are still many challenges and problems that need to be addressed.Future research should focus on improving the quantity and quality of data,improving the accuracy and reliability of weed identification,enhancing the generalization ability and robustness of deep learning modes,and guiding UAV to autonomously perform mapping to achieve collaborative operations between UAV and ground equipment.At the same time,the application research of deep learning technology of CNN in various aspects of actual production should be strengthened to provide more efficient and intelligent solutions for agricultural production.
NETL
NSTL
万方数据
