Deep Learning Detection of Weeds in Vegetable Fields
[Objective]Deep learning to accurately identify weeds for effective weeding in vegetable fields was investigated.[Method]Image of a vegetable field was cropped into grid cells as sub-images of vegetables,weeds,and bare ground.Deep learning networks using the ShuffleNet,DenseNet,and ResNet models were applied to distinguish the target sub-images,particularly the areas required weeding.Precision,recall rate,F1 score,and overall and average accuracy in identifying weeds of the models were evaluated.[Result]Although all applied models satisfactorily distinguished weeds from vegetables,ShuffleNet could simultaneously deliver a 95.5%precision with 97%recall and a highest detection speed of 68.37 fps suitable for real-time field operations.[Conclusion]The newly developed method using the ShuffleNet model was feasible for precision weed control in vegetable fields.
Vegetablesweedsimage treatmentdeep learningweeding area determination
国家科技期刊平台
NSTL
万方数据
维普
