Evaluation of cotton emergence using UAV-based imagery and deep learning
暂无分享,去创建一个
Jianfeng Zhou | Kenneth A. Sudduth | Aijing Feng | Earl Vories | K. Sudduth | Jianfeng Zhou | E. Vories | Aijing Feng
[1] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[2] F. Forcella,et al. Modeling seedling emergence , 2000 .
[3] F. Baret,et al. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. , 2017 .
[4] Rasmus Nyholm Jørgensen,et al. RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network , 2017 .
[5] Ronald Davis,et al. Neural networks and deep learning , 2017 .
[6] Vijay Kumar,et al. Counting Apples and Oranges With Deep Learning: A Data-Driven Approach , 2017, IEEE Robotics and Automation Letters.
[7] L. M. Bugayevskiy,et al. Map Projections: A Reference Manual , 1995 .
[8] Xin Zhang,et al. Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN , 2020, Comput. Electron. Agric..
[9] Surya Kant,et al. Estimation of crop plant density at early mixed growth stages using UAV imagery , 2019, Plant Methods.
[10] Richard O. Duda,et al. Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.
[11] Lori J. Wiles,et al. The cost of counting and identifying weed seeds and seedlings , 1999, Weed Science.
[12] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[13] S. Sankaran,et al. High-Resolution Aerial Imaging Based Estimation of Crop Emergence in Potatoes , 2017, American Journal of Potato Research.
[14] Kenneth A. Sudduth,et al. Comparison of electromagnetic induction and direct sensing of soil electrical conductivity , 2003 .
[15] K. Ghassemi-Golezani,et al. Effects Of Seed Vigor On Growth And Grain Yield Of Maize , 2014 .
[16] Ruizhi Chen,et al. Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images , 2018, Precision Agriculture.
[17] Xin Zhang,et al. Faster R–CNN–based apple detection in dense-foliage fruiting-wall trees using RGB and depth features for robotic harvesting , 2020 .
[18] Loris Nanni,et al. Handcrafted vs. non-handcrafted features for computer vision classification , 2017, Pattern Recognit..
[19] Urs Schmidhalter,et al. Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs) , 2017, Remote. Sens..
[20] Terry Griffin,et al. Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques , 2018, Remote. Sens..
[21] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[22] Lav R. Khot,et al. Field-based crop phenotyping: Multispectral aerial imaging for evaluation of winter wheat emergence and spring stand , 2015, Comput. Electron. Agric..
[23] Henrik Skov Midtiby,et al. Plant species classification using deep convolutional neural network , 2016 .
[24] Jianfeng Zhou,et al. Cotton Yield Estimation from UAV-Based Plant Height , 2019, Transactions of the ASABE.
[25] Xiuliang Jin,et al. A method to estimate plant density and plant spacing heterogeneity: application to wheat crops , 2017, Plant Methods.
[26] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[27] Andreas Kamilaris,et al. Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..
[28] Baskar Ganapathysubramanian,et al. An explainable deep machine vision framework for plant stress phenotyping , 2018, Proceedings of the National Academy of Sciences.
[29] Dennis B. Egli,et al. Seed Vigor and the Uniformity of Emergence of Corn Seedlings , 2012 .
[30] Jianfeng Zhou,et al. Evaluation of Cotton Emergence Using UAV-Based Narrow-Band Spectral Imagery with Customized Image Alignment and Stitching Algorithms , 2020, Remote. Sens..
[31] Marcel Salathé,et al. Using Deep Learning for Image-Based Plant Disease Detection , 2016, Front. Plant Sci..
[32] N. Otsu. A threshold selection method from gray level histograms , 1979 .
[33] S. Sankaran,et al. Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review , 2015 .
[34] Chenghai Yang,et al. Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery , 2018, Front. Plant Sci..
[35] Rui Li,et al. Improved Kiwifruit Detection Using Pre-Trained VGG16 With RGB and NIR Information Fusion , 2020, IEEE Access.
[36] Maryam Rahnemoonfar,et al. Deep Count: Fruit Counting Based on Deep Simulated Learning , 2017, Sensors.