J Appropr Technol > Volume 9(1); 2023 > Article
Pierre, Sefu, Venuste, D’Amour, Daniel, Pierre, Bosco, and Felix: Smart Crops Irrigation System with Low Energy Consumption


The COVID-19 preventive measurements restrict farm-workers from travel to their farms, social distancing and lockdown to prevent them from getting infected. Consequently, farming and irrigation activities are dramatically reduced due to shortage of farm-workers and reduction of crops production. To find solutions, various technologies have been adopted to remotely connect farmers with their farm-fields from their homes and perform the same work as they are in the farm. An Internet of Things (IoT) approach, modern sensor technologies, and smart irrigation equipment are coupled to allow the collection of the field data for automatic irrigation of the field. This paper presents the IoT system based on Fuzzy logic controller that assists farmers to receive and monitor necessary irrigation data from the farm field through their smart phones and PCs. The Wireless Sensor Networks (WSNs) are deployed in the field to measure the water content in the soil and water used for plants by measuring the soil moisture dynamics throughout the entire duration of the crop growing season in the Maize crop farm. The Fuzzy logic controller was used for data analytics and intelligent decision making as well as Fuzzy logic toolbox in MATLAB to simulate irrigation scheduling process. The system saves 50% of water and energy consumption for irrigation, 80% reduction of farm-workers and increase in 20% of crop production.


Agriculture sector is crucial to the population welfare as it provides the food, employment and raises the national economy; In Rwanda, about 96% of its population living in rural areas depends mainly on agriculture for their income and it is almost impossible to survive without this sector. However, the climate change, the irregular precipitation and long dry season are negatively affecting the quantity and quality of crop production and results in agriculture production crisis in the region. Each agricultural crop has its weather requirements for growing, that’s why the farmers wisely select the right season when to sow on the basis of weather condition (Zhu et al., 2020; Patil et al., 2019). Therefore, in order to achieve a high quantity and quality of crop production by balancing weather condition, irrigation is essential.
The method used in irrigation involves building canals which supply uniformly water from water resources or use of water pumps for spraying water evenly in the field. Nevertheless, the scarcity of water resources has been a problem that impacts the agriculture development. Therefore, A convenient irrigation system shall make the agricultural process more effective (Patil et al., 2019). However, not only the currently used methods of irrigation consume more water than what plants need, but also require farmers to manually and constantly operate water valves or water pumps in case of absence or excess of water in the field. In addition, COVID-19 pandemic as the biggest health crisis in this decade, due to its preventive measures taken by different organ around the world which restrict the people to stay at home (lockdown) and social distancing have severely affected agricultural activities (Filtjens, n.d.; GSMA, 2021). Therefore, the introduction of ICT in agriculture has emerged as the effective and sustainable solution to improve irrigation systems.
The emergence and rapid development of IoTs and Big Data Analytics presents interesting solutions to the above problem. (Badrun and Manaf, 2021; Salvi et al., 2017). In this context, an IoT system can connect multiple sensor nodes and other smart devices located in the field and link them to a local or cloud based controller. These nodes in turn collect and transmit real-time data from their surroundings to the controller. This information can be either used to offer for example a farmer both immediate and long term response or used by predictive and adaptive algorithms to execute operational responses such automate irrigation scheduler (Gautam and Sen, 2015; Jamroen et al., 2020).
The use of Smart Crops Irrigation System with Low Energy Consumption overcomes the above mentioned problems with An Internet of Things (IoT) approach, modern sensor technologies, and smart irrigation equipment are combined to permit the collection of the field data for automatic irrigation and notification . This study presents the IoT based irrigation system using Fuzzy logic controller that assists farmers to receive and monitor the field parameters such as soil moisture, and temperature as well as remotely controlling irrigation system installed in the farm field through their smart phones and PCs.

Related works

This section presents the cutting-edge studies for currently available related solutions. First, threatenings to farmers due to COVID-19 pandemic are evaluated followed by IoT technologies for irrigation and finally the applications of Fuzzy logic in agriculture are presented.

1. Threatening to farmers due to the COVID-19 pandemic

Threatening to farmers due to the COVID-19 pandemic has exacerbated, especially in developing countries. in Rwanda, big number of the citizen are employed in farming activities where the report shows that in 2019 in Rwanda, 64% of the population was hired in this agriculture sector (MINAGRI, 2020). Since outbreak of COVID-19, one among the effective measures to control the spread of the pandemic was to enforce the citizen to stay-at-home (4.I. Workshop: Health systems resilience during COVID-19: Lessons for building back better, 2021; Tripathi et al., 2021). The agriculture also was one among sectors affected by these COVID-19 prevention measures. One among affected farming parts was irrigation where farmers were restricted to go to irrigate their fields and had to depend again on rain season only which reduced dramatically their production (Boughton et al., 2021; Selim and Eltarabily, 2022). nevertheless the IoT was introduced and being used in agriculture sector, challenges of COVID-19 pandemic have proved that the digital agriculture need to be accelerated, where smart irrigation systems are needed for autonomous irrigation and assisting farmers in remote monitoring and tracking forming activities (GSMA, 2021; Nyakuri et al., 2022).

2. IoT connectivity technologies for irrigation

Wi-Fi connectivity is a widely used wireless communication technique. Many agricultural monitoring systems, such as the one presented in (Mendez et al., 2012) by Gerard Rudolph et al and (Hasan et al., 2021), employ Wi-Fi to communicate amongst the various agents of their designs. They demonstrated a Wi-Fi wireless sensor network for farm monitoring, with nodes collecting data on temperature, humidity, light, soil moisture, and water level. The data was also sent to a server so that it could be viewed later. As a result, researching MiFi coverage in various agricultural settings is of tremendous interest. In (Mendez et al., 2012; Li et al., 2020), Muhammad A. et al. reported a method for estimating the location of wireless nodes based on signal intensity. The IEEE 802.11 b/g standard was used and the locations included urban regions, rural areas, woodlands, and plantations. However, Wi-Fi requires more internet infrastructure when deployed in remote area.
Cellular system employs GSM/GPRS standard for data communication between field sensor node and the cloud server for data storage and further analysis. Additionally, the Android app is being developed to track the system's current state instantly (Science, 2018). In (Munir et al., 2021; Yang et al., 2021), the GSM module was used to connect an IoT cloud server to a network of sensors, analyzes the data at the edge server, transfers only a subset of the data to the main IoT cloud server to predict the amount of water that a field of crops will need, and uses an edge Android application to display the outcome. The IoT devices communicate with cellular networks by using second generation (2G), third generation (3G), fourth generation (4G) and GPRS standards due to its relatively long-range wireless communication and its robust communication links (Khattab et al., 2019; Science, 2018).
Bluetooth Low Energy enabled technology allows IoT devices in irrigation system to communicate effectively with minimized cost and energy consumption but within a short range less than 100 m (Kim et al., 2008), developed sensorbased variable rate irrigation systems with Bluetooth technology. The application in this article requires plug-and-play compatibility to serial devices with affordable wireless communication modules in order to accommodate existing data loggers and sensors. For wireless data transmission between in-field sensor stations and a base station, the Bluetooth module was chosen (Ilapakurti and Vuppalapati, 2015) and (Hasan et al., 2021) used the Bluetooth low energy framework to sensor sensor nodes. Their choice for using Bluetooth low energy, also known as Bluetooth Smart, was to maintain a same communication range as Wi-Fi but using significantly less power and cost. When compared to other previous stated technologies used in irrigation, system design optimization, transmission energy, cost and security can be achieved by improved with the only limitation of the device communication range.

3. Fuzzy logic for Data Management and Analytics for Irrigation Optimization

Fuzzy logic system determines degree of truth for right decision making to the problem as that of human perception and reasoning can make. Fuzzy logic algorithms are robust and adapt easily to the changing environments (Jane and Ganesh, 2019; Pezol et al., 2020) in (Phogat et al., 2021) an irrigation system based on ANN and Fuzzy Logic was developed. Soil moisture dynamics, precipitation, evapotranspiration for wheat crop were considered. And the results of Fuzzy logic was found suitable with 89% better performance due to better decision making the lower implementation cost. An intelligent automatic control system was developed in (Shongwe, 2022) and (Jamroen et al., 2020) using the fuzzy logic controller to enable smart irrigation based on environmental factors such as soil moisture variation, soil Ph, solar irradiation, air temperature, and air humidity. The system showed water and energy usage efficiency relatively to the optimal growth of the plant.
After observing irrigation challenges faced by formers during COVID-19 pandemic and reduction of their production due to those challenges, based on the necessity of digitalization in irrigation system and gaps identified in different researches, the Smart Crops Irrigation System with Low Energy Consumption was developed. It works by deploying sensor nodes in the field so that irrigation can be done based on water requirements by the plants to reduce water wastage from irrigating unneeded location and assisting farmers in irrigation activities using smart device.

System design

1. System architecture

The proposed system is illustrated in the Figure 1 and is designed for semi-automatic irrigation system. The sensor nodes are deployed in the field and work in WSN mode at the edge of the network and consist of sensors, Microcontroller and ZigBee technology and gather all necessary field data to a single gateway. The gateway gathers all data sends data to the cloud platform for further analysis. The water for irrigation came from AKAGERA River and stored in the water reservoir where it is pumped to the field by the solarpowered pump when it is needed by any sensor node in the field. For any sensor node, if the water content in the soil get below the threshold value, the irrigation is done either automatically or by the farmer according to the weather forecasting. The field data is sent to the cloud platform through the gateway using SGM system. The cloud plat form receives big data from the field and weather forecasting, analyses it, take decision and sent notification to the farmer through smart phone and PC. The farmer accesses to all field information and can operate the irrigation system from home (whether to irrigate or stop irrigation if it is going to rain) using his smart phone.

2. Solar powered sensor node

The sensor node presented in this project collects data from the given section of the field (for this case is 2 ha), processes it and communicates it to the gateway which is the central unit of the WSN. It consists of four main units; the sensing unit that employs Hobonet TEROS 12 Soil Moisture/Temperature/EC Sensor to detect the water content and temperature dynamics in the soil, Atmega 328 microcontroller which is responsible for processing data from sensors and determine the irrigation requirements, communication units using Xbee module for transmitting processed data to the gateway and finally the solar panel module for powering the whole device.

3. Wireless sensor network (WSN)

WSN is the distributed sensor nodes structure that allows many sensor nodes to communicate each other wirelessly in the network. In this study, the standalone sensor node can cover area not more than 2 hectares while the field is 12 hectares; so, the sensor nodes are connected in the WSN structure in order to collect the entire field information to the central unit (The gateway) for further processing and communicating the same data to the cloud for further analysis and decision making. The ZigBee technology has been chosen for their low power consumption, low cost and easy transmission compared to Lora, Wi-Fi and GSM technologies.

4. Mobile application

The bazafarm mobile application is found in the google play store. Its dashboard show the farm information and soil condition with notification to the farmer. The soil moisture level, soil electrical conductivity, the battery level of each device and the device ID is shown in the dashboard.

5. System flowchart

Smart crop irrigation operates by using microcontroller Atmega 328 which starts by scanning soil moisture, soil temperature, and electrical conductivity of the soil. Those three parameters were scanned with help of moisture sensor placed to in the soil. Therefore, those scanned parameters are sent to both cloud and to mobile phone by using GSM. When soil moisture becomes less than twenty four percent, then, turn on the pump for irrigating the crop. Otherwise pump is off. The electrical conductivity of soil turns pump on while it is becomes greater 570 ms/m.

Fuzzy logic controller and MATLAB simulation

1. Fuzzy logic system for decision making

Fuzzy logic approach is a machine learning technique for data analytics and intelligent decision making for uncertain problems based on the degree of truth. Fuzzy logic helps in right decision making as that can be made by human perception and reasoning based on the environment variation rather than convention true or false (1 or 0) logic (Arcos-Aviles et al., 2021).

2. Fuzzy inference system and membership functions

For the simulation of the output based on the input variation through the Fuzzy Logic Controller is done in the Fuzzy logic Toolbox in MATLAB (Alfin and Sarno, 2018). The output is the speed variation of the electrical pump for irrigation that determines the quantity of water to be irrigated according to the soil state. The soil state is determined by the soil moisture, soil temperature and the soil EC data collected by sensors as illustrated in Fuzzy Inference System editor Figure 5.
In the proposed work, there are three Fuzzy input membership functions including soil moisture, soil temperature and EC. The input membership functions with their linguistic variables, corresponding ranges and plots are illustrated in the Table 1.
The output membership function of the Fuzzy system is the variation speed of electrical pump that varies from 0 to 30% for low state, 30% to 60% for medium state and 60% to 100% for high state of its total power. The linguistic variables, corresponding ranges and plot are described in the Table 2.

3. Fuzzy IF-THEN Rules

In this study, the fuzzy IF-THEN rule system is based on the expert knowledge in the field of irrigation to manage the appropriate quantity of water to irrigate the farm field at realtime. The Fuzzy Rule System starts by determining water content in the soil (in %), t emperature (in °C) and EC (in mS/m) of the soil as input Fuzzy values. The input Fuzzy values are applied to IF-THEN rule system that determines the output Fuzzy values as described in IF-THEN Rule viewer Figure 6.

4. Surface view

The surface viewers of the proposed Fuzzy system are the three-dimensional output surfaces that shows output variations with their corresponding inputs. The surface views in Figure 7(a), Figure 7(b) and Figure 7(c) represent the output variation speed of the electrical pump during irrigation process that correspond to soil moisture/soil temperature, soil moisture/EC and soil temperature/EC respectively.

Results and Discussion

1. Results

The 6 sensor nodes were deployed in the Maize farm located at Nyagatare in Eastern province of Rwanda near the Akagera River. The C9 section of the farm covers 12 hectors and each sensor node is able to serve not more than 2 hectors section of the field. After smart irrigation system with low energy consumption installed in the Maize farm, the data collected in two consecutive seasons, the season 1 started from April to July 2021 and the season 2 started from August to October the results obtained are presented in the Figure 8.
1. The data collected by each sensor during those seasons show that the average of the soil moisture was 29.45% and 33.55% respectively, which are exactly in the expected normal range.
2. The average soil temperature was 22.56°C and 23.53°C respectively which correspond to the normal recommended temperature range.
3. Water usage and energy consumption was reduced by 45% due to the reduction of the irrigation time duration.
4. The 80% reduction in number of farm-workers due to automatic irrigation and remote farm irrigation monitoring system via smart phone.
5. 20% increase in maize production compare to previous seasons.
The resulting Averages of soil moisture and temperature in the period of two seasons showed that water content in the soil was maintained at desired level due to the regular automatic irrigation system based on the water needs for the plant.
Data were collected in two periods, the first period was from May to July and the second was from August to December and C9 section covers 12 Ha and the crop planted is Maize. Each sensor node sends data every 30 minutes and each single data packet has 2.5 kb size.
According to the above chart, the number of data collected in C9 section in season1 is estimated to 22.9 MB while number of data collected in season 2 is 18.5 MB. So, the number of data collected in season1 is more than season 2 because season 1 was longer than season two.

2. Discussion

The project was well conducted durig the two seasons periode which had defferent rainfall and sunshine patterns. The resulting average soil moisture and temperature in that period proved that water content in the soil was maintained at desired level due to the regular automatic irrigation system based on the water needs for the plant. By using this system, farmers can nolonger rely on rainfall condition. In addition, a significant benetit for farmers was achieved by saving 80% of labor force (as COVID-19 pandemic prevention measures were conserned) and 45% of energy consumption was saved with 20% increase in crop productivity.
By introducing this smart irrigation system, other types of crop than Maize such as vegetables and potatos can be grown and increase significantly the productivity. The availability of sufficient sunlight makes renewable enery an additive source of free energy for irrigation equipment to overcome electrical energy problem in the region.


This study presents smart irrigation project based on soil parameters dynamics suitable for Maize crop irrigation. It uses sensors to sense variation in moisture and temperature in the soil, process them using Raspberry-pi 3 and Fuzzy Inference Controller for decision making and gives recommendation (notification message) to the farmers according to standards predetermined through mobile application. This system provides capabilities for the farmer to monitor their farm irrigation activities anywhere they can be and at any time as the COVID-19 pandemic prevention measures prevent movement. The results shows that there are significant reduction of water quantity and energy for irrigation as well as preventing the COVID-19 transmission by monitoring the farm activities remotely through smart phone.

Figure 1.
Wireless sensor network architecture of the system.
Figure 2.
Solar powered sensor node.
Figure 3.
Mobile application dashboard.
Figure 4.
System flowchart.
Figure 5.
Fuzzy inference system editor.
Figure 6.
Fuzzy IF-THEN rule system.
Figure 7.
3D input-outout surface viewers.
Figure 8.
Average soil moisture and temperature by each of six sensor nodes of C9 section in season 1 and season 2 (in %).
Figure 9.
Average data transmitted by each of six sensor nodes of C9 section in season 1 and season 2 (in MB).
Table 1.
Table caption
Table 2.
Output membership functions and their linguistic variable ranges with plot
Input Membership functions Linguistic Variable Range (%) Membership Function for output
Variable speed electrical pump (%) LOW(L) 0-30 jat-2022-00192i2.jpg
MEDIUM(M) 30-60
HIGH(H) 34-50


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