DETERMINE AND CONTROL NUTRIENT DEFICIENCY OF PLANTATION IN HYDROPONIC SYSTEM
By
Yoki Andriawan Ramdan 2-1852-006
MASTER’S DEGREE in
MECHANICAL ENGINEERING - MECHATRONICS
FACULTY OF ENGINEERING AND INFORMATION TECHNOLOGY
SWISS GERMAN UNIVERSITY The Prominence Tower
Jalan Jalur Sutera Barat No. 15, Alam Sutera Tangerang, Banten 15143 - Indonesia
February 2021
Revision after Thesis Defense on 29 January 2021
Yoki Andriawan Ramdan STATEMENT BY THE AUTHOR
I hereby declare that this submission is my own work and to the best of my knowledge, it contains no material previously published or written by another person, nor material which to a substantial extent has been accepted for the award of any other degree or diploma at any educational institution, except where due acknowledgement is made in the thesis.
Yoki Andriawan Ramdan
_____________________________________________
Student
11 February 2021 Date
Approved by:
Dr. Ir. Widi Setiawan
_____________________________________________
Thesis Advisor
11 February 2021 Date
Dr. Edi Sofyan, B.Eng., M.Eng
_____________________________________________
Thesis Co-Advisor
11 February 2021 Date
Dr. Maulahikmah Galinium, S.Kom., M.Sc
_____________________________________________
Dean of Faculty of Engineering & IT
11 February 2021 Date
Yoki Andriawan Ramdan ABSTRACT
DEVELOPMENT OF EMBEDDED IMAGE PROCESSING TO CLASSIFY, DETERMINE AND CONTROL NUTRIENT DEFICIENCY OF PLANTATION IN
HYDROPONIC SYSTEM
By
Yoki Andriawan Ramdan Dr. Ir. Widi Setiawan, Advisor Dr. Edi Sofyan, B.Eng., M.Eng, Co-Advisor
SWISS GERMAN UNIVERSITY
One of required technology to growth Hydroponic system is how to quickly detect nutrient deficiencies in plants. In the traditional way, checking for nutrient deficiencies is done manually, using destructive method by taking sampling of the leaf. This requires a lot of time and effort, especially for large areas. In this thesis, Deep Convolutional Neural Network is used to diagnose Fe deficiency of Pak Choy. We compare Inception- ResnetV2 and MobileNetV2 architecture using transfer learning method with the fine- tuning model was carried out to train dataset which consist of 249 images for training and 28 images for testing. The result show that best accuracy from the training is achieve 98% for 2000 epochs using MobileNetV2 and was finally selected to deploy to the Raspberry PI 4B, with test in live capture image show accuracy 81%. The average prediction time process in live capture image about 3 seconds. The Fe Deficiency detection algorithm integrated with nutrient dosing control system able giving the feedback from Fe Deficiency detection to run the dosing pump A2 for Fe nutrient. The dosing control show good result, which is based on time calculation, able to run 5ml dosage in 10s with speed setting of dosing pumps 36 rpm.
Keywords: Nutrient deficiency, Deficiency detection, Deep Learning, Embedded system, Dosing control.
Yoki Andriawan Ramdan
© Copyright 2021 by Yoki Andriawan Ramdan
All rights reserved
Yoki Andriawan Ramdan DEDICATION
I dedicate this works for the future of hydroponics control development and the country I loved: Indonesia
Yoki Andriawan Ramdan ACKNOWLEDGEMENTS
I wish to thank the members of my family for their support and patience. Dr. Ir. Widi Setiawan asThesis Advisor was particularly helpful in guiding me toward a qualitative methodology. Dr. Edi Sofyan, B.Eng., M.Eng interest in sense of competence was the impetus for my proposal. Finally, I would like to thank Dena Hendriana, B.Sc., S.M., Sc.D. as Head of Program Study Master of Mechanical Engineering whom from the beginning, he had confidence in my abilities to not only complete a degree but to complete it with excellence.
I have found my coursework throughout the Curriculum and Instruction program to be stimulating and thoughtful, providing me with the tools with which to explore both past and present ideas and issues.
Yoki Andriawan Ramdan TABLE OF CONTENTS
Page
DEDICATION ... 5
ACKNOWLEDGEMENTS ... 6
CHAPTER 1 – INTRODUCTION ... 13
1.1 Background ... 13
1.2 Research Problem ... 16
1.3 Objectives ... 17
1.4 Hypothesis ... 18
1.5 Limitation ... 18
CHAPTER 2 - LITERATURE REVIEW ... 19
2.1 Plant Nutrient Requirements in Hydroponics ... 19
2.2 Nutrient Deficiency Identification ... 21
2.3 Hydroponic Control Systems ... 22
2.4 Image Classification in Deep Convolutional Neural Network ... 25
CHAPTER 3 – RESEARCH METHODS ... 32
3.1 Control and Treatment Row Setup ... 32
3.2 Control System Setup ... 34
3.3 Temperature, Electrical Conductivity and Dosing Pumps calibration ... 42
3.3.1 Temperature Sensor Calibration ... 43
3.3.2 Electrical Conductivity Sensor Calibration ... 44
3.3.3 Dosing Pump Calibration ... 46
3.4 Development Dosing Control and Monitoring ... 47
3.5 Development of Image classification using Convolutional Neural Network ... 50
3.6 Image dataset acquisition... 55
CHAPTER 4 – RESULTS AND DISCUSSIONS ... 61
4.1 Image Classification Training Result ... 61
4.2 Deployment CNN result in Raspberry Pi 4B ... 68
4.3 Software for CNN Testing and Raspberry Programming ... 70
4.4 Dosing System result ... 72
4.5 EC and Temperature Sensor Monitoring ... 73
Yoki Andriawan Ramdan CHAPTER 5 – CONCLUSIONS AND RECOMENDATIONS ... 74 5.1 Conclusions ... 74 5.2 Recommendations ... 74
Yoki Andriawan Ramdan LIST OF FIGURES
Figures Page
Example work of M. A. Beck, C.-Y. Liu, C. P. Bidinosti, C. J. Henry, C. M. Godee,
and M. Ajmani [8] ... 15
Example of Circuit Diagram of Lettuce Control [11] ... 16
Example of Pakcoy (Brassica rapa) with nutrient deficiency (A) compare with healthy one (B) ... 17
Example of DFT system ... 19
Fe-Deficiency reported by [16] ... 21
Pak Choy with Fe Deficiency[14] ... 22
typical of pH Sensor ... 23
Typical of EC Sensor ... 23
DS18B20 ... 24
Simple Nutrient Control for DFT ... 24
Example of leaf image analysis and selection area of interest method [19] ... 26
The image of a rice canopy captured by a digital camera and the same image processed using Matlab: (a) original image, (b) scaled green channel minus red channel (GMR) value and displays as image and (c and d) segmented images using GMR threshold 15 and 30, respectively. Black portion of the images is regarded as non-canopy (soil and plant residues). ... 26
CNN image classification pipeline [20] ... 27
MobileNetV2 Building Block [24] ... 28
Network Schema of InceptionResnetV2[23] ... 29
Regulation on the triangular learning rate. The blue lines reflect values of the learning rate that change across boundaries. The step-size input parameter is the number of iterations in half a cycle.[28] ... 30
Proposing of Plant identification and classification by UAV or UGV [30] ... 31
Flow Chart of Thesis Work ... 32
Yoki Andriawan Ramdan
Row Setting - Perfective View - unit of measure in cm ... 33
Row Setting - Top View - unit of measure in cm ... 33
Typical Setup for each row ... 34
Row Setup ... 34
Raspberry PI 4B ... 35
EC using TDS Meter Card ... 37
Raspberry PI Camera ... 37
ADS1115 shield for Raspberry PI ... 38
Intllab 12vDC 100 Rpm Peristaltic Pump ... 38
L298N Motor Driver Pin Out ... 39
Wiring Diagram ... 41
Thesis Bench work in two different treatment period ... 42
Temperature Calibration Listing ... 43
Verification Result between DS18B20 and Digital Thermometer ... 44
Python program to convert the voltage value to the correct EC Value ... 45
Python program to read voltage value from ADS1115 analogue channel ... 46
5mL dosing test ... 46
Dosing Pump Calibration Python Code ... 47
Flow Control Diagram for Dosing System ... 48
EC Dosing Control GUI ... 49
Stage development of Image Classification ... 51
A1, A2 and B Stock solution ... 58
Camera and Compact hand-held EC, pH and Temperature meter used during thesis work ... 58
Pak Choy with Fe-Deficiency from 1st Treatment Schedule ... 59
Normal Pak Choy from 1st Treatment Schedule ... 60
Pak Choy with Fe-Deficiency from 2nd Treatment Schedule ... 60
Normal Pak Choy from 2nd Treatment Schedule ... 60
Result Map from Thesis Work ... 61
Confusion Matrix Result MobileNetV2, A. 100 epochs, B. 300 epochs, C. 500 epochs, D. 1000 epochs and E. 2000 epochs ... 63
Confusion Matrix Result InceptionResnetV2, A. 100 epochs, B. 300 epochs, C. 500 epochs, D. 1000 epochs and E. 2000 epochs ... 64
Yoki Andriawan Ramdan Loss function comparation after apply optimum learning rate between MobileNetV2
2000epochs[A] and InceptionResnetV2 2000 epochs[B] ... 65
Ground Truth Prediction test MobileNetV2 with 2000 epochs ... 66
Top Losses from Training Test MobileNetV2 with 2000 epochs ... 67
Ground Truth Prediction test InceptionResnetV2 with 2000 epochs ... 67
Top Losses from Training Test InceptionResnetV2 with 2000 epochs ... 68
Prediction Result in by Raspberry Pi Camera ... 69
Fe-Deficiency test in 3062 Lux ... 69
Fe-Deficiency prediction test in 1442 Lux ... 70
Error During Run Fastai V2 ... 71
EC Dosing Control using Guizero ... 71
EC and Temperature Trend in Thingspeak ... 73
Yoki Andriawan Ramdan LIST OF TABLES
Table Page
Nutrient classification ... 13
pH and EC requirement for some plant[14] ... 20
Confusion matrix ... 30
Wiring Interface List ... 40
EC Sensor Row A Calibration ... 45
EC Sensor Row B Calibration ... 45
Type of Fertilizer used for Nutrient Setup ... 56
Hoagland Concentrate Setting in ppm ... 57
Amount of each fertilizer to create the nutrient ... 57
Nutrient Treatment schedule ... 59
Training Result MobileNetV2 ... 61
Accuracy Result MobileNetV2 ... 62
Training Result InceptionResnetV2 ... 62
Accuracy Result InceptionResnetV2 ... 63
Export file size comparation between MobileNetV2 and InceptionResnetV2 both with 2000 epochs ... 66
