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A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting

Plant Phenomics 2019年第1期1卷 1-14页

作者： sambuddha ghosa Bangyou Zheng Scott Chapman Andries B.Potgieter David R.Jordan Xuemin Wang Asheesh K.Singh Arti Singh Masayuki Hirafuji Seishi Ninomiya Baskar Ganapathysubramanian Soumik Sarkar Wei Guo Department of Mechanical Engineering Iowa State UniversityAmesIAUSA Department of Computer Science Iowa State UniversityAmesIAUSA CSIRO Agriculture and Food St.LuciaQLDAustralia School of Agriculture and Food Sciences The University of QueenslandGattonQLD 4343Australia Queensland Alliance for Agriculture and Food Innovation(QAAFI) The University of QueenslandGattonQLDAustralia Queensland Alliance for Agriculture and Food Innovation(QAAFI) The University of QueenslandWarwickQLDAustralia Department of Agronomy Iowa State UniversityAmesIAUSA International Field Phenomics Research Laboratory Institute for Sustainable Agro-Ecosystem Services

The yield of cereal crops such as sorghum(Sorghum bicolor ***)depends on the distribution of crop-heads in varying branching ***,counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding ***,measuring such phenotypic traitsmanually is an extremely labor-intensive process and suffers from low efficiency and human ***,the process is almost infeasible for large-scale breeding plantations or *** learning-based approaches like deep convolutional neural network(CNN)based object detectors are promising tools for efficient object detection and ***,a significant limitation of such deep learningbased approaches is that they typically require a massive amount of hand-labeled images for training,which is still a tedious ***,we propose an active learning inspired weakly supervised deep learning framework for sorghum head detection and counting from UAV-based *** demonstrate that it is possible to significantly reduce human labeling effort without compromising final model performance(R^(2)between human count and machine count is 0.88)by using a semitrained CNN model(i.e.,trained with limited labeled data)to perform synthetic *** addition,we also visualize key features that the network *** improves trustworthiness by enabling users to better understand and trust the decisions that the trained deep learning model makes.

关键词： Unmanned aerial vehicles (UAV)

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