Real-time evaluation of different indexes in precision agriculture using a heterogeneous embedded system

Abstract In this work, we present a real-time embedded implementation of an algorithm dedicated to monitoring agricultural fields. This algorithm is based on normalized indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The problem of most algorithms in this context is real-time processing, especially when we talk about applications that require time precision. The proposed implementation is based on the application of a Hardware/Software Co-design approach. For the embedded platform, we used the heterogeneous system contains CPU and GPU type XU4 and TX1. In this context, we used the parallel programming language OpenMP to have an optimal embedded implementation. The results showed that we could process 66 images/s using a desktop, 20 images/s in the XU4, and 17 images/s for TX1.

[1]  Marco Dubbini,et al.  Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..

[2]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[3]  Stefania Bonafoni,et al.  Spatiotemporal Analysis of MODIS NDVI in the Semi-Arid Region of Kurdistan (Iran) , 2019, Remote. Sens..

[4]  Roland Siegwart,et al.  weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming , 2017, IEEE Robotics and Automation Letters.

[5]  Prince Waqas Khan,et al.  UAV’s Agricultural Image Segmentation Predicated by Clifford Geometric Algebra , 2019, IEEE Access.

[6]  Mohamed Akil,et al.  Optimized implementation of real-time image processing algorithms on field programmable gate arrays , 1998, ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344).

[7]  Rachid Latif,et al.  SLAM algorithms implementation in a UAV, based on a heterogeneous system: A survey , 2019, 2019 4th World Conference on Complex Systems (WCCS).

[8]  Jiankun Hu,et al.  Multi-Resolution Weed Classification via Convolutional Neural Network and Superpixel Based Local Binary Pattern Using Remote Sensing Images , 2019, Remote. Sens..

[9]  Dong Liang,et al.  An Integrated Skeleton Extraction and Pruning Method for Spatial Recognition of Maize Seedlings in MGV and UAV Remote Images , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[10]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[11]  Jian Wang,et al.  Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm , 2019, Remote. Sens..

[12]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[13]  R. Jackson,et al.  Interpreting vegetation indices , 1991 .

[14]  Dehai Zhu,et al.  Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids , 2019, Remote. Sens..

[15]  F. López-Granados,et al.  Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management , 2013, PloS one.

[16]  Luis Enrique Sucar,et al.  Assessing Nitrogen Nutrition in Corn Crops with Airborne Multispectral Sensors , 2017, IEA/AIE.

[17]  Reza Ehsani,et al.  Applications of High-Resolution Imaging for Open Field Container Nursery Counting , 2018, Remote. Sens..

[18]  Kenneth C. McGwire,et al.  Optimized Stratification for Mapping Riparian Vegetation in Arid and Semiarid Environments , 2019, Remote. Sens..

[19]  Maggi Kelly,et al.  Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs) , 2019, Remote. Sens..

[20]  Alejandro Duran,et al.  The Design of OpenMP Tasks , 2009, IEEE Transactions on Parallel and Distributed Systems.

[21]  Fenglei Fan,et al.  A Hierarchical Extraction Method of Impervious Surface Based on NDVI Thresholding Integrated With Multispectral and High-Resolution Remote Sensing Imageries , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Srikanth Prabhu,et al.  Raspberry Pi as Visual Sensor Nodes in Precision Agriculture: A Study , 2019, IEEE Access.

[23]  V. Chéret,et al.  Soil and climate differently impact NDVI patterns according to the season and the stand type. , 2019, The Science of the total environment.

[24]  R. Latif,et al.  Evaluation of Agricultural Precision Algorithms on UAV Images , 2019, 2019 International Conference of Computer Science and Renewable Energies (ICCSRE).

[25]  Emilio Chuvieco,et al.  BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data , 2014, Remote. Sens..

[26]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[27]  Alfonso Niño,et al.  A Survey of Parallel Programming Models and Tools in the Multi and Many-core Era , 2022 .

[28]  Gregory Asner,et al.  Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2 , 2019, Remote. Sens..

[29]  Junho Yeom,et al.  Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture , 2019, Remote. Sens..

[30]  Oleh Pidhirniak,et al.  Automatic Plant Counting using Deep Neural Networks , 2019 .

[31]  Juha Suomalainen,et al.  Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  Massimo Menenti,et al.  Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water , 2019, Remote. Sens..

[33]  Adel Hafiane,et al.  Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images , 2018, Remote. Sens..

[34]  Debashis De,et al.  Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas , 2018, IEEE Internet of Things Journal.

[35]  Hiroshi Nakano,et al.  Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs , 2019, Remote. Sens..

[36]  Federico Viani,et al.  Low-Cost Wireless Monitoring and Decision Support for Water Saving in Agriculture , 2017, IEEE Sensors Journal.

[37]  Marcello Chiaberge,et al.  Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment , 2019, Remote. Sens..

[38]  Jorge Torres-Sánchez,et al.  An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery , 2018, Remote. Sens..

[39]  Yongil Kim,et al.  Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks , 2018, Remote. Sens..

[40]  Sergio Fagherazzi,et al.  Variations in Persistence and Regenerative Zones in Coastal Forests Triggered by Sea Level Rise and Storms , 2019, Remote. Sens..

[41]  Xing Guo,et al.  Parallel Computation of Aerial Target Reflection of Background Infrared Radiation: Performance Comparison of OpenMP, OpenACC, and CUDA Implementations , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[42]  Lianru Gao,et al.  Water Body Extraction from Very High Spatial Resolution Remote Sensing Data Based on Fully Convolutional Networks , 2019, Remote. Sens..

[43]  P. Roy,et al.  Tropical forest cover density mapping , 2002 .