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This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
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# Smart Lysimeter with Crop and Environment Monitoring
## Enhanced with Pest and Crop Control

Carlos Almeida¹, João C. Martins¹, João Miguel Santos¹, and José Jasnau
Caeiro¹

Instituto Politécnico de Beja, Beja, Portugal

carlos.almeida@ipbeja.pt

**Abstract.** A model of a smart lysimeter, adopting an IoT approach, enhanced with pest and crop state analysis is presented. Besides the measurement of the traditional evaporation-transpiration balance, the lysimeter senses additional parameters like the soil temperature and humidity at different depths; air temperature and humidity; sunlight exposure (visible and infrared). Additionally, the system can capture high-resolution images of the target culture. These images are locally processed for data reduction and the main features are stored in a remote platform afterwards. The main goal is the monitoring and enhancement of the global crop yield. This lysimeter also provides data for a global water resources system that integrates information from several sources: lysimeters, weather stations, water quality monitoring systems, etc.

**Keywords:** Smart Lysimeter, Precision Agriculture, IoT, Water Management, Evapotranspiration

## 1 Introduction

Modern agriculture is supported by sensor networks and the Internet of Things (IoT) is part of this trend, bringing new types of sensors, reducing its cost and providing large amounts of relevant data. This opens the possibility for novel types of data analysis related with crop and pest control, with the application of new types of pattern recognition techniques and the use of approaches based on artificial intelligence, namely machine learning techniques.

The use of the IoT paradigm allows the monitoring of crops in real-time, making possible to generate early alerts from the analysis of the collected data to support timely decisions by the farmer related with crop and pest control and harvesting [12].

Water is fundamental for socio-economic development, paramount for the production of food, for energy generation, for the maintenance of healthy ecosystems, and, as a result, essential for the humankind survival.

Given its scarcity, careful management of the water resources, particularly in its use in agriculture, which is responsible on average for 70 percent of all freshwater consumption [6], has become more and more important.
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The lysimeter is an important instrument to measure the watering needs for a particular type of culture, climate and soil. Lysimeters measure the evapo-transpiration (ET) of plants and have been used in agriculture for more than a century. Their primary purpose is to quantify the water requirements of plants in order to provide only the amount of water needed for their healthy growth.

Evapotranspiration is the quantity of water loss by the plants through evap-
oration and through transpiration. In a culture, there is a water balance due to
the water inflow and outflow. In the input water there is water from rain, irri-
gation and condensation. In the output water there is the drained water, that
infiltrates into the soil, the evaporation and transpiration. Figure 1 illustrates
the water cycle system in plant culture.

<figure><img src="image_2.png" /><figcaption>Fig. 1. Evapotranspiration diagram.</figcaption></figure>

Until about a decade ago, lysimeters were mostly used by educational and
research institutions due to their relatively high cost, but the emergence of new
technologies, materials and sensors, allowed the construction of high-tech and
low-cost weighing lysimeters. The use of a smart lysimeter which, in addition
to the traditional measuring of ET, senses other parameters related to the soil,
atmosphere and the culture, which were not previously available, with a low
production and installation cost, enhances its widespread use with the potential
increase in crop yield in terms of quality and quantity with the optimization of
resources, namely water.

In [14], the ET of desert plants is quantified using simple moisture sensors at various depths and pressure sensors associated to a microcontroller to visualize data and keep the soil moisture at a certain level. The presented lysimeter do not use water weighing.

To quantify the ET of large fields, [13] combines data from remote sensing, meteorological data, and a traditional lysimeter with weight sensors, making it possible to estimate the ET of large crops. In [8], a smart ET estimation is presented. The authors employ a fuzzy neural network to estimate the ET of a
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greenhouse plantation from the temperature, humidity and atmospheric pressure data features. After training the neural network and calibration of the system with a local weighing lysimeter, the ET is predicted.

When a high precision estimate of the ET is required, a weighing lysimeter is preferred. It is usually composed of two vessels, one with the soil with the plants and a second vessel that collects the drainage water. By measuring and comparing the weight of the soil vessel with the weight of the drainage vessel, it can estimate the ET with high precision. Different soils exhibit different water infiltration rates. At start, the infiltration rate is fast but, as the water moisturizes the soil, it becomes stable, and the use of a weighing lysimeter allows to assess both situations with increased accuracy. The use of several soil moisture sensors at different depths allows the characterization of the water percolation along the soil depth. In [7], an accurate water infiltration balance is presented recurring to a precision weighing lysimeter demonstrating that the weighing lysimeters present the best estimation for the ET in terms of precision.

From the image processing and analysis of the growing plants it is possible to infer its health and to identify and control pests. In [9], a study of application of image analysis techniques is described, where the most promising techniques to detect diseases on the plants' leaves and fruits, and to classify the disease, are presented. The paper [10], makes an extensive review about the application and importance of machine vision in precision agriculture. In [11] an image processing system is described to identify the type of plant from the leaves' veins.

This paper describes a smart lysimeter that, additionally to the ET, measures additional ambient and soil parameters, namely: the air temperature and humidity; the visible luminosity; the infra-red luminosity; the soil temperature and the humidity at different depths. Additionally acquires images of the target culture for posterior analysis for crop and pest control.

The remaining of this paper describes the architecture and the instrumentation, in terms of hardware and software, of a smart lysimeter. Section 2 presents the general architecture of the smart lysimeter; Section 3 describes the implementation of the experimental prototype in terms of its structure, hardware and software; Section 4 presents the obtained experimental results and the paper ends with the conclusions in Section 5.

## 2 Smart Lysimeter Architecture

A drawing of the physical structure of the smart lysimeter is presented in Figure 2. It is structurally composed by three different vessels complemented with a set of sensors. The top vessel contains the soil with the plants. The middle vessel collects the wastewater drained from the soil of the top vessel whose weighing allows the calculation of the ET. This wastewater is transposed afterwards to the bottom vessel so that it can be later used for further chemical and physical analysis. As an example, it can be used for a percolation study.

The lysimeter architecture is designed with currently available IoT technologies, namely: low cost sensors, low power microcontrollers (MCU) and long range
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<figure><img src="image_3.png" /><figcaption>Fig. 2. Lysimeter physical structure.</figcaption></figure>

communications. The design has the purpose of achieving a low cost and high performance system by following the IoT paradigm. A layered view of the system architecture is displayed in Figure 3. It can be decomposed into three main parts: the *i*) physical layer, where the sensors acquire data and where some early processing is done, which forwards the information to the cloud layer; *ii*) the cloud layer that is responsible for: the communications, the storage and data analysis, thus generating alarms related to the crop and pest control; and finally, at the top, *iii*) the application layer provides services to the different types of users according to their specific role.

Since there is the need to have different polling times to collect the data and to execute different functions, the physical layer is decomposed into two modules: the lysimeter module and the camera module.

The lysimeter module contains the sensors to measure the soil and environmental parameters, namely the soil temperature and the humidity at different depths. It weighs the soil vessel and the drained water to calculate the evapotranspiration, and measures the ambient temperature, the humidity and the light intensity. The sensors are connected to a low-power MCU that manages the acquisition and transmission of data to the cloud through a radio-frequency telecommunication SoC module. The MCU also manages and supervises the power supply by powering on all circuits when acquiring, processing and transmitting the sensors' data to the cloud layer. Afterwards, it powers off the subcircuits (sensors and SoC) in order to save power when the module is in standby, and turns on power again after the preset time polling.

The camera module is responsible for the image acquisition from the camera sensor. The camera sensor is connected to a SBC that acquires, preprocess and sends images to the cloud layer using an Internet connection. The SBC power is controlled by a low-power MCU to save energy: the MCU starts up the SBC when it is programmed to take an image (1 or 2 times a day is enough); after this, the MCU powers off the SBC to save energy.
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<figure><img src="image_4.png" /><figcaption>Fig. 3. A layered view of the system architecture.</figcaption></figure>

The cloud layer manages the data reception from different lysimeters. It encompasses database storage and the different services related to data processing, like pattern recognition, namely machine learning for event prediction. It triggers alarms related to crop monitoring and/or pest control. At the top is the application layer. Its purpose is to provide different services to end users, like system configuration, data analysis and visualization, alarms setting and display, etc.
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<figure><img src="image_5.png" /><figcaption>Fig. 4. Lysimeter system hardware architecture.</figcaption></figure>

A lower level system hardware architecture is depicted in Figure 4. The set of acronyms displayed in the figure and their related meanings are presented in Table 1.

<table><caption>Table 1. Lysimeter hardware acronyms.</caption><thead><tr><td>Description</td><td>Description</td></tr></thead><tbody><tr><td>MCU Microcontroller Unit</td><td>SoC System on Chip (WiFi)</td></tr><tr><td>ATS Ambient Temperature Sensor</td><td>AHS Ambient Humidity Sensor</td></tr><tr><td>ALS Ambient Light Sensor</td><td>STS Soil Temperature Sensor</td></tr><tr><td>SMS Soil Moisture Sensor</td><td>SWS Soil Weight Sensor</td></tr><tr><td>DWS Drained water Weight Sensor</td><td>SM Waste Vessel Servo Motor</td></tr><tr><td>SBC Single Board Computer</td><td>CS Camera Sensor</td></tr></tbody></table>
# 3 Experimental Prototype

The described IoT based lysimeter architecture led to a prototype that is described in this section. Low-cost off-the-shelf components, without compromising the technical quality of the final result, were chosen for the implementation of the lysimeter prototype, bringing a clear cost benefit. Open source software tools were adopted whenever possible.

## 3.1 Lysimeter Module

The hardware structure of the lysimeter module is displayed in Figure 5. A description and the listing of the components used to implement the lysimeter, along with its most important specifications, is presented in Table 2. An MCU from the MSP430 family, the MSP430G2553, is chosen, due to its very low power consumption and cost. It provides analog and digital ports to connect and control sensors. It has an I2C communications bus, used for sensors with I2C support, and an UART interface for connection to the SoC. The soil vessel is equipped with a set of corrosion resistant humidity and moisture capacitive sensors (CSMS), placed at different depths. The soil vessel is weighted using four load cells (4×50 kg load cells). These are part of the branches of a half Wheatstone electrical bridge, connected to one channel of a HX711 amplifier/ADC, that communicates with the MCU using a proprietary clocked digital output. The other channel of the HX711 module is connected to a load cell (1×10 kg) that weights the drained water vessel. The lysimeter has an ambient light sensor, the TSL2561, that integrates visible, and visible plus infrared sensing. The system also measures ambient temperature and humidity with a SHT30 sensor.

The module is powered from a 1W solar panel and stored in a MR18650 Li-Ion battery cell. To control the battery charge it is used a TP4056 module. The power switch is done with a DMP2022LS P Channel MOSFET transistor, that can control up to 10A. To control the drain water valve it is used a commercial HS422 servo-motor controlled by a PWM signal. For the communications with the cloud it is used a Wi-Fi SoC ESP8266-01 module. The set of components adopted for the prototype implementation is presented in Table 2.

The MSP430 MCU also manages the power provided to the different subsystems. It monitors the battery voltage and powers ON/OFF the sensors and the telecommunications SoC. The MCU also activates a servo motor which controls the valve that transfers the water from the drained water vessel to the wastewater vessel. The MSP430 MCU also sends the sensors' data to the ESP8266 module through the UART connection. The ESP8266 has a MQTT client application to transmit the data to the cloud. If pressed quickly, the multifunction button wakes up the module and a new sensor read loop is started. The MCU and telecommunications SoC software is developed with the GNU C programming language toolchain.
## 3.2 Camera Module

The hardware structure of the camera module is displayed in Figure 6. A description and listing of the main components used to implement the module, along with its most important specifications, are presented on Table 3.

This module also uses the MSP430G2553 MCU, from the MSP430 family, to supervise the battery status and the power of the camera module, namely of its SBC.

The SBC is a Raspberry Pi 3B+ (RPi) which is a compact, yet powerful small computer on a single board, with extensive wireless communications (Wi-Fi and Bluetooth) and a MIPI interface that can be connected to a series of different image cameras, besides a set of GPIO pins. The RPi is responsible for the image acquisition and pre-processing. It is equipped with a high resolution (8Mpx) Raspberry Pi Camera Module 2, with a IMX219 Sony image sensor. It makes a local backup of the images and sensors' data and sends it periodically to the cloud. The module's power system is similar to the one of the lysimeter module, complemented with a DC/DC converter to supply the SBC with the proper voltage (+5V). The module also possesses a multifunction button that when pressed wakes up the module and a new sensor read cycle is started automatically. The RPi has three software libraries installed: pahoMQTT client, Pyserial, and Picamera. It runs a set of Python scripts to control the system: acquire images, communicate with MCU, and communicate with the cloud.

A pair of photos of the system is presented in Figure 8. The lysimeter and the associated camera can be seen in these pictures. The camera setup is presented with greater detail in the picture on the right side of figure.

This experimental prototype uses Wi-Fi communications. However, for a system to be deployed in the open field, other communications solutions, like LoRa/LoRAWAN[5], NB IoT [2], or SIGFOX [3] can be easily adopted to transmit small packets of data. To transfer images, a higher band communications protocol must be used like an Internet LTE connection, for instance.

The camera module can be used as stand-alone system, to acquire and process images that can be provided to a previously trained machine learning model, running directly on the SBC, that can trigger alerts related to the crop state and culture health, and send them to the cloud and, ultimately, reach the user/farmer.

## 3.3 Lysimeter Software

The MCU software is developed with the Code Composer Studio suite [1], with the C/C++ programming language. The software is used for the control of data acquisition. The information is sent to the SBC using serial communications.

The SBC is running the Raspberry Pi OS, a Debian Linux operating system for ARM based architectures. The image acquisition and processing applications are developed using the Python programming language. They depend on the PiCamera package and also on the OpenCV Python bindings. A *crontab* script launches the applications at the predefined times. The images transfer is
<figure><img src="image_6.png" /><figcaption>Fig. 5. Lysimeter module hardware diagram.</figcaption></figure>

secured by the SSH protocol using public-key cryptography. Whenever the SBC
is powered up it waits for the MCU data, converting it to a JSON (Javascript
Object Notation) object that is sent to a MQTT server. Afterwards a picture
is taken, locally processed and sent to the storage server. After this sequence of
procedures the SBC is shut down. In case the data cannot be sent to the remote
server, the software stores a backup locally.

For the prototype's cloud software proof of concept, a virtual machine was
created. It runs the Ubuntu Server 20.04 LTS Linux distribution. All the soft-
ware services are launched as Docker containers. This Docker software stack
is composed by the *mosquitto* MQTT server; the Node-RED low code Node.js
based server; the MongoDB non relational database server; the TensorFlow CNN
(Convolutional Neural Network) machine learning based application and the AT-
MOZ/SFTP secure file storage server. This container technology based approach
ensures an easy and reliable software services deployment stack, as seen in Fig-
ure 7.
<table><caption>**Table 2.** Lysimeter module hardware components</caption><thead><tr><th></th><th>Description</th><th>Model</th><th>Specification</th></tr></thead><tbody><tr><td>MCU</td><td>Microcontroller</td><td>MSP430G2553</td><td>16 MHz, 16KB/512B</td></tr><tr><td>SoC</td><td>System on Chip</td><td>ESP8266-01</td><td>Wi-Fi 3.3VDC</td></tr><tr><td>ATS</td><td>Ambient Temp. Sensor</td><td>SHT30</td><td>(I2C) -40°C - 80°C, ±0.3°C</td></tr><tr><td>AHS</td><td>Ambient Humid. Sensor</td><td>SHT30</td><td>(I2C) 0-100% RH, ±2.0%</td></tr><tr><td>ALS</td><td>Ambient Light Sensor</td><td>TSL2561</td><td>(I2C) Visible and IR sensor</td></tr><tr><td>STS</td><td>Soil Temperature Sensor</td><td>DS18B20</td><td>(1 Wire) -10°C - 85°C, ±0.5°C</td></tr><tr><td>SMS</td><td>Soil Moisture Sensor</td><td>CSMS</td><td>(An) Grove Capacitive Sensor</td></tr><tr><td>SWS</td><td>Soil Vessel Weight Sensor</td><td>HX711</td><td>4 cells with 1/2 bridge</td></tr><tr><td>DWS</td><td>Drained Water Weight Sensor</td><td>HX711</td><td>1 cell - Wheatstone Bridge</td></tr><tr><td>SM</td><td>Servo Motor</td><td>HS422</td><td>(PWM) 180°, torque 3.3Kg/cm</td></tr><tr><td>SL</td><td>Photovoltaic Solar Panel</td><td>FAL09004</td><td>5VDC 1W 110x60mm</td></tr><tr><td>BCC</td><td>Battery Controller Charge</td><td>TP4056</td><td>1A 1BAT Li-Ion</td></tr><tr><td>BT</td><td>Li-Ion Battery</td><td>INR18650-35E</td><td>3.6V 3500mAh Samsung</td></tr><tr><td>PSW</td><td>Power Switch</td><td>DMP2022LSS</td><td>-10A P-MOSFET</td></tr><tr><td>DCC1</td><td>DC/DC Converter MCU</td><td>MCP1700-3302</td><td>3.3V 250mA LDO</td></tr><tr><td>DCC2</td><td>DC/DC Converter</td><td>MCP1700-3302</td><td>3.3V 250mA LDO</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Camera module hardware diagram.</figcaption></figure>

## 4 Experimental Results

The prototype is assembled as described, including its programming and configuration. A photo of the prototype being used to monitor strawberry plants, with a detail of the camera set (right-top) and the water vessels (right-bottom) is shown in Figure 8.
<table><caption>**Table 3.** Camera module hardware components</caption><thead><tr><th></th><th>Description</th><th>Model</th><th>Specification</th></tr></thead><tbody><tr><td>CS</td><td>Camera Sensor</td><td>RPi Cam. Mod. 2</td><td>IMX219 8 Mpx sensor</td></tr><tr><td>SBC</td><td>Single Board Computer</td><td>RPi 3B+</td><td>1.4 GHz BCM2837 4GB</td></tr><tr><td>MCU</td><td>Microcontroller</td><td>MSP430G2553</td><td>16 MHz, 16KB/512B</td></tr><tr><td>SL</td><td>Photovoltaic Solar Panel</td><td>FAL09004</td><td>5VDC 1W 110x60mm</td></tr><tr><td>BCC</td><td>Battery Controller Charge</td><td>TP4056 Module</td><td>1A 1xBAT Li-Ion</td></tr><tr><td>BT</td><td>Li-Ion Battery</td><td>INR18650-35E</td><td>3.6V 3500mAh Samsung</td></tr><tr><td>PSW</td><td>Power Switch</td><td>DMP2022LSS</td><td>-10A P-MOSFET</td></tr><tr><td>DCC1</td><td>DC/DC Converter</td><td>MCP1700-3302E</td><td>3.3V 250mA LDO</td></tr><tr><td>DCC2</td><td>DC/DC Converter</td><td>VMA402 Module</td><td>5V 2A Step-Up LM2577</td></tr></tbody></table>

<table><caption>**Fig. 7.** Cloud Services Virtualization</caption><thead><tr><th>Broken MQTT</th><th>WEB Tools</th><th>Database</th><th>Machine Learning</th><th>SFTP</th></tr><tr><th>Mosquitto</th><th>Node Red</th><th>MongoDB</th><th>TensorFlow</th><th>atmoz/sftp</th></tr></thead><tbody><tr><td></td><td></td><td>Docker Container</td><td></td><td></td></tr><tr><td></td><td></td><td>Ubuntu Server VM</td><td></td><td></td></tr><tr><td></td><td></td><td>Virtualization Software</td><td></td><td></td></tr><tr><td></td><td></td><td>Operating System</td><td></td><td></td></tr><tr><td></td><td></td><td>Hardware</td><td></td><td></td></tr></tbody></table>

The current values of the gathered data are visualized in a dashboard using a computer based web browser client or from a smartphone, Figure 10. The values that are collected during the previous 2 days period are made available with the Node-RED Dashboard web server, as can be seen in Figure 11.

A high resolution image of the plants, acquired by the camera module, is shown in Figure 12. On the right, a detailed image of the fruit is presented, from where it is possible to detect, by a trained eye, a significant set of plant diseases. However, the goal is to have a machine learning based system, running in the cloud servers, able to detect the state of the fruits, the possible plant diseases, and to automatically fire up an alarm and send the corresponding alert to the users by email or SMS.

The web service is compatible with modern PWA (Progressive Web Apps[4]). It is thus possible to access the lysimeter data from any mobile device through a smartphone app similar interface.

The images are stored using the SFTP (Secure File Transfer) protocol. An example of a captured image from strawberry plants can be seen in Figure 12.

From experience, the sensors' data can be collected with a time interval between 10 min to 30 min, and since the plant evolution is very slow it is enough to capture one or two images per day, by choosing the time of day with the best light conditions.
<figure><img src="image_8.png" /><figcaption>**Fig. 8.** Prototype appearance and some hardware details (right figure).</figcaption></figure>

# 5 Conclusions

The lysimeter prototype presented shows the application of the IoT paradigm to solve a common and fundamental problem in agriculture – the measurement of the evaporation-transpiration balance. Additionally, several other physical parameters of the soil and surrounding environment, and the collection of images for monitoring the plants’ evolution are added. This data can be used to detect diseases and plagues using a machine learning based system. The proposed lysimeter system offers an integrated low-cost and energy efficient solution for the evaporation-transpiration balance measure that can be used in different locations and scenarios.

The next steps in the development of the smart lysimeter are the collection of a large dataset, imagery and sensor data, from crop and soil monitoring, to develop and train a machine learning model to detect if plants are growing as expected or if there are problems related to the crop development and potential diseases. The integration of the smart lysimeter into an overall water resources system that gathers information from various other sources, including weather stations, water quality monitoring systems, water evaporation monitoring systems, lake and river water level monitoring systems, etc. is also planned. A data model based on modern pattern recognition systems, machine learning, and arti-
<figure><img src="image_9.png" /><figcaption>**Fig. 9.** NodeRed flow (graphical program), showing the data acquisition process.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 10.** NodeRed dashboard output (Web Browser/SmartPhone).</figcaption></figure>

ficial intelligence software will allow to relate all data collected in order to predict
events and monitor long-term threats to water resources.
<figure><img src="image_11.png" /><figcaption>Fig. 11. NodeRed dashboard with graphs of the last 24h data.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 12. Example of a captured raw image (left) with a zoomed detail (right).</figcaption></figure>

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