INTERNENT OF THINGS (IOT) BASED SMART AGRICULTURE MONITORING SYSTEM FOR ENHANCED PRODUCTIVITY

2.3.1 Temperature Sensor

4.1.3  Soil moisture sensor:

4.1.4   Water level sensor: 

4.1.5.  MATLAB simulink: 

References

Appendix 1: Temperature Sensor Code

Appendix 2: Humidity Sensor Code

Appendix 3: Soil Moisture Sensor Code

Appendix 4: Water Level Sensor Code

Appendix 5: Motion Detection Sensor Code

Appendix 6: Complete Simulation Code

Fig. 3.1: Block Diagram of the IoT Based Smart Agriculture System

Fig. 3.2: Design Architecture for the IoT Based Smart Agriculture System

Fig. 3.3:  Flowchart of the IoT Smart Monitoring System Operation

Fig 4.1: Temperature Sensor displaying set values

Fig 4.2: Temperature Sensor displaying status as “Too Low”

Fig 4.3: Temperature Sensor displaying status as “Ok”

Fig 4.4: Temperature Sensor displaying status as “Too High”

Fig 4.5: Humidity Sensor displaying set values

Fig 4.6: Humidity Sensor displaying status as “Too Low”

Fig 4.7: Humidity Sensor displaying status as “Ok”

Fig 4.8: Humidity Sensor displaying status as “Too High”

Fig 4.9: Soil Moisture Sensor displaying set values

Fig 4.10: Soil Moisture Sensor displaying status as “Too Low”

Fig 4.11: Soil Moisture Sensor displaying status as “Ok”

Fig 4.12: Soil Moisture Sensor displaying status as “Too High”

Fig 4.13: Water Level Detector displaying set value

Fig 4.14: Water Level Detector displaying status as “Ok”

Fig 4.15: Water Level Detector displaying status as “Low”

Fig 4.4: Water Level Sensor Results

Fig 4.16: Motion Detector displaying status as “Ok”

Fig 4.17: Motion Detector displaying status as “Motion Detected”

Fig 4.18. The IOT Simulation as a whole showing integrated sensors

Fig 4.19: The simulation on Simulink

Figure 4.20a: Plot of Temperature against Time

Figure 4.20b: Plot of Temperature against Time gotten from tradition tomato farm

Figure 4.21a Plot of Humidity against Time

Figure 4.21b Plot of Humidity against Time gotten from traditional tomato farm

Figure 4.22: Plot of Soil Moisture against Time.

Figure 4.23: Plot of Water Level against Time

Figure 4.24: Plot of Motion level against Time

Table 4.1: Temperature Sensor Results

Table 4.2:  Results of Humidity Sensor

Table 4.3: Soil Moisture Sensor Results 

Agriculture is the basic means of survival for people all around the world, because it basically caters for what we eat. We also get fabrics for our clothing and timber for our shelter from agriculture; hence its importance can never be overemphasised. In past decades, it was observed that there was not much crop development in agriculture sector, causing food price to continuously increase because crop production rate was declining. These have pushed millions of people into poverty. There are number of factors which could be responsible for this problem some of which includes; weather change, low soil fertility, diseases, inadequate monitoring of the agricultural process, etc.

It then becomes very essential to make effective intervention in agriculture and a solution to this problem is Smart Agriculture. This is achieved by installing electronic equipment in a farm that collect and analyze data from the farm and send it through wireless protocol to the farmer. The collected data provide information about the various environmental factors. But monitoring the environmental factors is not the complete solution to increase the yield of crops. There are other factors that decrease the productivity to a greater extent. Hence automation must be implemented in agriculture to overcome these problems (Suma, et al, 2017). So, in order to provide solution to all such problems, it is necessary to develop an integrated system which will take care of all factors affecting the productivity in every stage. This project deals on developing smart agriculture using Internet of Things (IoT).

loT integration with Wireless sensor networks have potentials to change the way of development in agriculture and give more contributions to smart agriculture (Patil and Kale, 2016). Internet of Things (IoT) involves a three-tier system. It includes perception layer, network layer and application layer. Perception layer includes sensor motes. Information Communication Technology (lCT) enabled devices, like sensor motes are building blocks of sensor technology. It includes cameras, Radio-Frequency Identification (RFID) tags, sensors and sensor network used to recognize objects and collecting real time information. The network layer is an infrastructure of the loT to realize universal service. It combines the perception layer and application layer. The latter is a layer that combines the loT with the technology of specific industry. IoT is applied in all areas of industry, including smart agriculture, engineering, smart parking, smart building environmental monitoring, healthcare, transportation and many more. Agriculture is a vital area which targets millions of people Patil and Kale, (2016)

Agriculture apart from being a source of livelihood for people all around the world, it has particularly been one of the primary occupations in Nigeria for ages, and before Petroleum was discovered, it was the leading source of income for the Nigerian government, providing employment for about 30% of the Nigerian population as of 2010 and contributed 32% to Gross Domestic Product (GDP) in 2001 (Yakubu and Akanegbu, 2015). But now due to migration of people from rural to urban areas, and due to the believe by many that agriculture is stressful even with the use of machineries and many other factors, there has been hindrance in the development of agriculture. So, to overcome this problem we go for smart agriculture techniques using Internet of Things (IoT). Hence, the need for this Project.

As the world moves towards new technologies and implementations it is important to apply same in agriculture; This Project will discuss how various features like remote controlled monitoring, humidity/temperature sensing and monitoring, intruders-checking, security, proper irrigation facilities, etc. can be deployed on a farm to increase productivity and reduce man hour.

This project is set to design and simulate Internet of Things (IoT) based technology in monitoring a crop plantation for improved productivity.

The objectives are;

This project is focused on enhancing agriculture using IOT based smart monitoring systems. It covers modeling an all suitable IOT based smart agriculture monitoring system to monitor a tomato farm. The model will be such that it can be applicable to other crop type only by adjusting the sensor parameters as required. 

Although a complete model will be presented, this project will not cover implementation in hardware but simulation with Proteus Simulator will be done to show system operation.

The importance of smart monitoring cannot be overemphasized as it helps make farming easier and on the long run help increase productivity. This is achieved as smart monitoring permit timely intervention on issues developing on the farm before they escalate to bigger problems and cause destruction of crops, some of these issues may include; High Temperature, low humidity, low soil moisture, pest/intruder attack, etc.