Kalman Filtering for Accurate and Fast Plant Growth Dynamics Assessment

Artificial growth systems are the essential part of the precision agriculture. It allows solving many problems associated with the growing demand in the environmental friendly food production in the context of increasing world population. Accurate and reliable assessment of plant growth dynamics parameters is crucial for the future success of the whole growing system parameters optimization. In this research, we report on the implementation of the extended Kalman filtering method for insitu evaluation of plant growth dynamics parameters. We show the reliability and benefits of the proposed approach on the simulated and experimental data obtained from the IoT-based testbed. We demonstrate that our method serves as a robust and computationally cost-effective tool for the accurate assessment of the growing dynamics that, in turn, could be used for the further optimization of the whole plant cultivation process in artificial conditions.

[1]  Rijo Jackson Tom,et al.  IoT based hydroponics system using Deep Neural Networks , 2018, Comput. Electron. Agric..

[2]  Edward Jones,et al.  Improved image processing-based crop detection using Kalman filtering and the Hungarian algorithm , 2018, Comput. Electron. Agric..

[3]  Amol P. Bhagat,et al.  Review on Precision Agriculture using Wireless Sensor Network , 2015 .

[4]  Joan Rieradevall,et al.  COMPARING THE ENVIRONMENTAL IMPACTS OF GREENHOUSE VERSUS OPEN-FIELD TOMATO PRODUCTION IN THE MEDITERRANEAN REGION , 2008 .

[5]  Spatio-temporal population control applied to management of aquatic plants , 2019, Ecological Modelling.

[6]  Alexey Voinov,et al.  A review of methods, data, and models to assess changes in the value of ecosystem services from land degradation and restoration , 2016 .

[7]  Harald Schuh,et al.  Application of Kalman filtering in VLBI data analysis , 2015, Earth, Planets and Space.

[8]  Hanno Scharr,et al.  Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner] , 2015, IEEE Signal Processing Magazine.

[9]  Andrey Somov,et al.  Pervasive Agriculture: IoT-Enabled Greenhouse for Plant Growth Control , 2018, IEEE Pervasive Computing.

[10]  Lin Kaiyan,et al.  A Review on Computer Vision Technologies Applied in Greenhouse Plant Stress Detection , 2013 .

[11]  Andrey Somov,et al.  Designing Future Precision Agriculture: Detection of Seeds Germination Using Artificial Intelligence on a Low-Power Embedded System , 2019, IEEE Sensors Journal.

[12]  Roberto Passerone,et al.  Towards Extending Sensor Node Lifetime with Printed Supercapacitors , 2012, EWSN.

[13]  Klaus Diepold,et al.  Nonlinear state estimation for suspension control applications: a Takagi-Sugeno Kalman filtering approach , 2017 .

[14]  Xiang Chen,et al.  Distributed Kalman filtering over wireless sensor networks in the presence of data packet drops , 2017, 2017 American Control Conference (ACC).

[15]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[16]  Hiroshi Mineno,et al.  Greenhouse Environmental Control System Based on SW-SVR , 2015, KES.

[17]  Tomás Martínez-Marín,et al.  Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy , 2014, IEEE Geoscience and Remote Sensing Letters.

[18]  Andrey Somov,et al.  Wireless multi-sensor gas platform for environmental monitoring , 2015, 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) Proceedings.

[19]  Stefanos Kollias,et al.  Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments , 2019, Acta Horticulturae.

[20]  Rupert Gerzer,et al.  Pervasive agriculture: Measuring and predicting plant growth using statistics and 2D/3D imaging , 2018, 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[21]  Li Sheng,et al.  Reliable Data Fusion of Hierarchical Wireless Sensor Networks With Asynchronous Measurement for Greenhouse Monitoring , 2019, IEEE Transactions on Control Systems Technology.

[22]  Jens Hauslage,et al.  Enabling Precision Agriculture Through Embedded Sensing With Artificial Intelligence , 2020, IEEE Transactions on Instrumentation and Measurement.

[23]  Konstantinos P. Ferentinos,et al.  Wireless sensor networks for greenhouse climate and plant condition assessment , 2017 .

[24]  Arnold J. Bloom,et al.  Easy Leaf Area: Automated digital image analysis for rapid and accurate measurement of leaf area1 , 2014, Applications in plant sciences.

[25]  Zhang Xiaodong,et al.  Nondestructive measurement of total nitrogen in lettuce by integrating spectroscopy and computer vision , 2015 .

[26]  Rajnikant V. Patel,et al.  Computer Vision Based Autonomous Robotic System for 3D Plant Growth Measurement , 2015, 2015 12th Conference on Computer and Robot Vision.

[27]  Guangjun Liu,et al.  Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and extended Kalman filtering , 2016, Neural Computing and Applications.

[28]  M. V. Kulikova,et al.  The Accurate Continuous-Discrete Extended Kalman Filter for Radar Tracking , 2016, IEEE Transactions on Signal Processing.

[29]  Hiroshi Mineno,et al.  A Reliable Wireless Control System for Tomato Hydroponics , 2016, Sensors.

[30]  Syahril Efendi,et al.  Remote monitoring system for hydroponic planting media , 2017, 2017 International Conference on ICT For Smart Society (ICISS).