Early corn stand count of different cropping systems using UAV-imagery and deep learning
暂无分享,去创建一个
Jianfeng Zhou | Kenneth A. Sudduth | Newell R. Kitchen | Chin Nee Vong | Lance S. Conway | N. Kitchen | K. Sudduth | Jianfeng Zhou | C. N. Vong | L. Conway
[1] Daniel Racoceanu,et al. Corn classification using Deep Learning with UAV imagery. An operational proof of concept , 2018, 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence (ColCACI).
[2] 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..
[3] Hui Feng,et al. Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection , 2020, Remote. Sens..
[4] Derek Bradley,et al. Adaptive Thresholding using the Integral Image , 2007, J. Graph. Tools.
[5] K. Sayre,et al. The role of conservation agriculture in sustainable agriculture , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.
[6] Andreas Kamilaris,et al. Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..
[7] Jefferson R. Souza,et al. Corn Plant Counting Using Deep Learning and UAV Images , 2019, IEEE Geoscience and Remote Sensing Letters.
[8] F. Baret,et al. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. , 2017 .
[9] J. Lauer,et al. Optimum Plant Population of Bt and Non-Bt Corn in Wisconsin , 2006 .
[10] Ciro Potena,et al. UAV Image Based Crop and Weed Distribution Estimation on Embedded GPU Boards , 2019, CAIP Workshops.
[11] Wei Guo,et al. Aerial Imagery Analysis – Quantifying Appearance and Number of Sorghum Heads for Applications in Breeding and Agronomy , 2018, Front. Plant Sci..
[12] Brian L. Steward,et al. Using Aerial Hyperspectral Remote Sensing Imagery to Estimate Corn Plant Stand Density , 2006 .
[13] Maoguo Gong,et al. Automatic Tobacco Plant Detection in UAV Images via Deep Neural Networks , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[14] Jeffrey A. Coulter,et al. Agronomic Responses of Corn to Planting Date and Plant Density , 2011 .
[15] Xin Zhao,et al. Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging , 2019, Sensors.
[16] P. C. Robert,et al. Field evaluation of a corn population sensor. , 2000 .
[17] 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..
[18] Ping Wang,et al. Segmentation of Green Vegetation in the Field Using Deep Neural Networks* , 2018, 2018 13th World Congress on Intelligent Control and Automation (WCICA).
[19] J. Six,et al. Productivity limits and potentials of the principles of conservation agriculture , 2014, Nature.
[20] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[21] Xinliang Tian,et al. Detection of ground straw coverage under conservation tillage based on deep learning , 2020, Comput. Electron. Agric..
[22] Yared Assefa,et al. Yield Responses to Planting Density for US Modern Corn Hybrids: A Synthesis-Analysis , 2016 .
[23] Robert Broz,et al. Long-term agroecosystem research in the central Mississippi river basin: introduction, establishment, and overview. , 2015, Journal of environmental quality.
[24] Gabriela Csurka,et al. What is a good evaluation measure for semantic segmentation? , 2013, BMVC.
[25] Urs Schmidhalter,et al. Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs) , 2017, Remote. Sens..
[26] Baskar Ganapathysubramanian,et al. A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting , 2019, Plant phenomics.
[27] Jinshui Zhang,et al. Capturing Maize Stand Heterogeneity Across Yield-Stability Zones Using Unmanned Aerial Vehicles (UAV) , 2019, Sensors.
[28] Donald G. Bullock,et al. Soil Temperature and Planting Date Effects on Corn Yield, Leaf Area, and Plant Development , 1996 .
[29] Kristen S. Veum,et al. Cropping System, Landscape Position, and Topsoil Depth Affect Soil Fertility and Nutrient Buffering , 2018 .
[30] Ruizhi Chen,et al. Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images , 2018, Precision Agriculture.
[31] M. Ryan,et al. No-till and cropping system diversification improve soil health and crop yield , 2018, Geoderma.
[32] John D. Kelleher,et al. A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease , 2019, Front. Neurosci..
[33] N. Kitchen,et al. Long‐Term Impacts of Cropping Systems and Landscape Positions on Claypan‐Soil Grain Crop Production , 2016 .
[34] Tareq Abed Mohammed,et al. Understanding of a convolutional neural network , 2017, 2017 International Conference on Engineering and Technology (ICET).