PRT581 Final Report Assignment 339601

PRT581 PRINCIPLES OF SOFTWARE SYSTEMS

PROJECT REPORT

Smart Agriculture System:

Submitted By

Yash Bhaveshbhai Balar - s398712

MD SHAHAJADA HASIB - s397670

Submitted To

Lecturer Name

CHARLES DARWIN UNIVERSITY

FACULTY OF SCIENCE AND TECHNOLOGY

DD Month 20__

Abstract

This report presents an overview of the Design of Smart Agriculture System utilizing Internet of Things (IoT) to modernize the traditional agriculture system and to increase the agriculture productivity. The system uses field sensors to capture the moisture level, ambient temperature, humility and light level readings in different zones. Such environmental-science data are sent to a central gateway from which they are sent to a cloud-based engine for real-time crop analysis.  Some of the main features include automated irrigation, which will turn on the irrigation system if the moisture levels in the ground drop and help save up to 30% water. Such machine learning algorithms analyze images of leaves to identify plant pathogens at its early stages before the leaves are hugely damaged. The farmer engages with these insights via its intuitive dashboard, which displays performance metrics, history trends and yield analytics to highlight results.  The platform is based on a block structure with React and React Native being used for client interfaces. The relational data is stored in PostgreSQL, while the sensors log their data as time-series in InfluxDB, and the backend APIs are served by Node.js. Scalability, role-based access control and data encryption are major structural needs that must be considered. Comprehensive unit, integration and user acceptance tests ensure that the system runs smoothly in all climatic conditions. This solution provides analytical information for farmers, reduces operating costs and enables sustainable food production.

Ethical Consideration

Ethical concepts and requirements have been involved in the development of this Smart Agriculture System. All forms of data gathering follow privacy laws and any information about the users and any farm data is kept confidential and is stored securely by industry standard encryption. The system's design also accounts for sustainability and supports water conservation by are precisely irrigating it, whilst minimizing chemical runoff through customized fertilization advice.

The project is compliant with IEEE Code of Ethics for software engineers, which requires software developers to ensure their work follows the highest professional standards, be independent in conducting their professional judgments, and act in the best interest of the public. This project has been built using all third-party libraries, frameworks and APIs which have been licensed under open-source or academic-use licenses, whereby the full text of the license and authorship are described in the references section of this report.

The Project has been documented throughout the project and is open and accurate. Design choices, test procedure and outcomes have been noted in a consistent and accurate way for replication and traceability. The team has also been mindful of the opportunity's societal impact, for example, on the use of manual farm work, and designed the system to augment rather than replace human expertise in making farming decisions.

Table of Contents

1. Introduction. 7

1.1 Project Overview.. 7

1.2 Purpose. 7

1.3 Project Deliverables. 8

1.4 Feasibility Study. 8

1.4.1 Technical Feasibility. 8

1.4.2 Financial Feasibility. 9

2. The Scope of the Work. 9

2.1 Benefits. 9

2.2 Limitations. 9

2.3 Competing Products. 10

2.4 Roles and Responsibilities 10

2.5 Tools and Techniques. 10

3. Software Requirements. 11

3.1 Product Use Cases. 11

3.1.1 Use Case Diagrams. 11

3.1.2 Product Use Case List 11

3.2 Functional Requirements. 12

3.3 Non-Functional Requirements. 13

3.3.1 Performance Requirements. 13

3.3.2 Maintenance and Support Requirements. 13

3.3.3 Security Requirements. 14

3.3.4 Scalability Requirements. 14

3.3.5 Legal Requirements. 14

3.4 Milestones. 14

3.5 Risks and Contingencies. 15

3.6 Operating Environment 15

3.7 Assumptions and Dependencies. 16

3.8 Timetable. 16

4. Software Design. 17

4.1 Design Overview.. 17

4.1.1 Sequence Diagram.. 17

4.1.2 Class Diagram.. 17

4.1.3 E-R Diagram.. 17

4.1.4 Activity Diagram.. 18

4.2 System Architecture Design. 18

4.2.1 Chosen System Architecture. 18

4.2.2 Chosen Design Pattern. 18

4.2.3 Alternative Design Pattern. 19

4.3 User Interface Design. 19

5. Test Plans. 22

5.1 Features to be tested / not to be tested. 22

5.2 Pass/Fail Criteria. 22

5.3 Approach. 22

5.4 Testing materials (hardware / software requirements) 23

5.5 Test Cases. 23

5.5.1 Test Case 1 - Automated Irrigation Trigger 23

5.5.2 Test Case 2 - Crop Disease Detection. 24

5.5.3 Test Case 3 - Dashboard Real-Time Updates. 24

5.6 Testing Results. 25

5.7 Conclusion. 25

5.8 Recommendation. 25

Appendices. 27

Appendix 1: Smart Agricuture System - Use Case Diagram.. 27

Appendix 2: Sequence Diagram.. 28

Appendix 3: Class Diagram.. 29

Appendix 4: ER Diagram.. 30

Appendix 5: Activity Diagram.. 31

Appendix 6: System Architecture. 32

Appendix 7: User Interface Design. 33

References. 36

1. Introduction

1.1 Project Overview

The Smart Agriculture System is a technology solution based on IoT that addresses the challenges that exist in modern agriculture. The world's population will reach 9.7 billion by 2050 and food production must be boosted, while at the same time utilizing scarce natural resources, including water, land, and the climate (Worldbang.org, 2025). The traditional methods of farming, traditionally based on 'trail and error', manual observation and experience-based decision making are proving less and less adequate to satisfy the need for productivity and sustainability (Li et al. 2025).

It is a project to design and create a comprehensive environment monitoring sensor, automated irrigation control mechanism, crop disease monitoring function and even pesticide monitoring function and control mechanism in one system to form “smart agriculture platform”. This platform has the ability to gather data from IoT sensors placed throughout agricultural fields, analyze the data using sophisticated algorithms and present actionable insights to farmers on an easily navigable web interface and a mobile application. The system can automate repetitive tasks in agricultural production, analyze their current state and propose rational management recommendations based on the analysis of measured changes, which will in turn help them optimize the yields of their crops, avoid wasting resources and be ready for environmental changes and potential threats.

The components of this system are interconnected, with a sensor network capturing environmental variables, the cloud-based backend facilitating data processing and storage, automated actuators handling irrigation and fertilization management, and user applications monitoring and managing everything. The protocol of each component is standardized to allow the components to communicate with each other, and the protocol is also used for interoperability and ease of maintenance. The system also has the ability to perform predictive analysis, which is able to detect trends in historic crop conditions to identify future potential problems and create solutions to mitigate it.

1.2 Purpose

The main purpose of this project is to create and design a smart agriculture system based on technology which will enable farmer to perform real-time monitoring of environmental parameters and manage resources automatically and intelligently. The primary goal of the system is to close the gap between classic farming methods and precision farming concepts, offering an affordable, easily accessible and scalable approach for farms of varying size and resource level.

In particular, the system aims for the following goals: (1) to enable continuous monitoring of environmental conditions using a network of sensors based on IoT technology for measuring soil moisture, temperature, humidity, and light levels; (2) to automate the scheduling of irrigation based on real-time soil moisture data, thereby optimising the use of water; (3) to ensure early detection of plant diseases by analysing images and applying machine learning algorithms; (4) to offer integrated farm performance analysis through an interactive dashboard and mobile application; and finally, (5) to support predictive maintenance scheduling for farm machinery and infrastructure.

1.3 Project Deliverables

●      Fully functional Smart Agriculture System web application with dashboard for monitoring a farm

●      An iOS/Android mobile app that allows for remote farm management

●      Integration of temperature, humidity, soil moisture and light sensors for IoT

●      Automated irrigation control system, programmable threshold and timer system

●      Detection of crop diseases based on the (copy of) images classification using machine learning algorithms

●      Complete System Documentation such as design specifications and a user manual

●      Test plans, test cases, and testing results that show that the system is reliable

1.4 Feasibility Study

1.4.1 Technical Feasibility

The platform utilizes tried and tested technologies with robust community support, ensuring a high degree of stability. React and React Native work fine with web dashboard and mobile interfaces. High volume sensor streams are processed efficiently with NodeJS and Express in the backend infrastructure. The light MQTT protocol is being utilized to achieve data transmission, the perfect choice for the bandwidth-limited rural environment (De et al. 2025). Relational databases (such as PostgreSQL) store user records that are accessed many times, and time series databases (such as InfluxDB) store time series data representing environmental conditions. The crop abnormality is detected via machine learning models developed on TensorFlow / PyTorch (Haq et al. 2023). Open-source tools abate licensing restrictions, and cloud systems are easy to scale up.

1.4.2 Financial Feasibility

This analysis identifies low setup cost, and high return margins for farmers. The initial hardware cost is a combination of the sensor nodes, which range in cost from $15 to $40 per node, and a central node (gateway device). Cost is kept low due to the use of open source tools and free hosting levels. Services run continuously on the cloud and need a monthly investment based on the size of deployment. Water use is reduced by 30% and this leads to very fast water savings per year due to automated irrigation. Whenever feasible, the early identification of disease type enables a cushion against major decline in crop production from a season's cycle. The first capital investment, therefore, has a short payback period.

2. The Scope of the Work

2.1 Benefits

The platform presents remarkable economic, environmental and social benefits for today's farming communities. In the mostly economic aspect, automatic watering systems can save up to 40% of the water used in watering. This is a resource management that reduces costs of operation and provides high seasonal harvest. On an environmental level, data-driven insights help avoid the run-off of chemical fertilizers and ensure long term soil health (Ugwu et al. 2025). Socially, the system enables precision farming technology to be introduced in smaller enterprises, fuelled by the lack of enterprise capital. Simple mobile user interface means that they are highly accessible which minimizes manual labor workload a great deal.

2.2 Limitations

However there are limitations to the operation of the smart platform that will need to be continually attended to technically. Reliable internet connectivity is integral to the services on the back end as it enables real-time data transfer. The transmissions of central alerts are delayed with extended network outages, even with gateway data caching. Moreover, machine learning algorithms need extensive datasets, and scarce, hard to pinpoint crop diseases could lead to misclassification (Ngugi et al. 2024). There is a need for regular maintenance of physical sensor hardware to address issues with battery depletion and calibration drift. Last, missing-out environmental variables such as soil pH and pest measurements limit overall management scope (Mellander et al. 2025). The challenges point to the iterative learning areas of development.

2.3 Competing Products

There are multiple market entrants who are competing in the smart agriculture market. FarmLogs, recently integrated under JDOps Center, monitors and analyzes farm data, but is geared toward larger commercial farms with a higher cost threshold. CropX offers high-end technology in soil sensors with cloud analysis, however the company's disease detection technology is not as well developed. Granular by DuPont provides farm management and financial planning, but does not support real-time integration of IoT sensors.

The Smart Agriculture System proposed demonstrates its superiority with the integration of environmental monitoring, automated control of irrigation systems, disease detection and pest control, all on a single, cost-effective platform. As opposed to most of the other competitive systems available which focus on big commercial farms, this one is designed to be easily adopted and value scalable to small to medium farms. The open source technology also has advantages over proprietary technology in terms of customization and integration options and flexibility.

2.4 Roles and Responsibilities

Table 1: Role and Responsibilities

(Source: Self-Developed)

2.5 Tools and Techniques

Table 2: Tools and Techniques

(Source: Self-Developed)

3. Software Requirements

3.1 Product Use Cases

3.1.1 Use Case Diagrams

The Use Case Diagram below shows how the main actors interact with the main system functions. The diagram shows all the user-system interaction, including the interactions between use cases via include and extend stereotypes [Refer to Appendix 1].

3.1.2 Product Use Case List

Table 3: Product Use Case List

(Source: Self-Developed)

3.2 Functional Requirements

Table 4: Functional Requirements

(Source: Self-Developed)

3.3 Non-Functional Requirements

3.3.1 Performance Requirements

●      For every sensor data change, the system should process the data and show the updated data within two seconds of the sensor sending the data to the server.

●      The Web desktop shall be accessible within 3 seconds on normal broadband internet.

●      The API shall provide for the simultaneous submission of a hundred sensor data without degradation.

●      Owership of the mobile app shall not have more than 500 milliseconds of latency for any interaction with the application by the end user.

3.3.2 Maintenance and Support Requirements

The system shall be designed to be modular to simplify and isolate changes and maintenance for the parts. Comprehensive inline documentation shall be provided and all software components contain well documented coding standards. The system shall generate comprehensive logs of any critical events, errors, or exceptions that occur in the system, for the purposes of information to aid in troubleshooting. A maintenance plan shall be created for regular patching of security updates and database optimisation and calibration of sensors (Khan et al. 2025).

3.3.3 Security Requirements

All communication amongst IoT devices, back end server, and client applications shall be encrypted with TLS 1.2 or latest. Passports for users will be bcrypt-hashed and will have at least twelve rounds. The system shall have a three level of access; Farmer, Administrator and System. API endpoints will need access tokens that will have a specified time period. All user information shall be sanitized and validated before it is used within the system to avoid injection attack.

3.3.4 Scalability Requirements

The system will be able to scale to handle growth of backend services. The database layer shall facilitate database growth via table partitioning along with read replicas. The IoT gateway should be able to connect up to 200 sensors simultaneously, with 500 sc or more available by using multiple IoT gateway instances. The cloud shall use auto-scaling policies to adapt the amount of compute resources depending on their required demand pattern (Alharthi et al. 2024).

3.3.5 Legal Requirements

The system will ensure adherence to the Australian Privacy Act 1988 in regards to the collection, storage, and use of personal information (Oaic.gov.au, 2025). Any information collected from farms will be owned by the farmers and will not be passed on to third parties without written authorization. The system should ensure that there is an audit trail of all data accesses and changes to it for accountability. All the open-source components constructed in a project should adhere to their respective licensing terms; all the license information should be documented in the project's repository.

3.4 Milestones

Table 5: Milestones

(Source: Self-Developed)

3.5 Risks and Contingencies

Table 6: Risks and Contingencies

(Source: Self-Developed)

3.6 Operating Environment

The system starts to run on various platforms.The system runs in several contexts. Such sensors must be robust and have IP65 or greater ingress protection ratings, operating temperatures from minus 20°C to plus 60°C and a battery life of more than six months in normal operation. The IoT gateway is housed in a protected area on the farm that has either wired Ethernet, wireless internet or cell phone data connectivity (Bayih et al. 2022).

The server stack is built on top of cloud services, like AWS or Google Cloud, and is based on containers (Docker) and high-available orchestration (Kubernetes). The web dashboard can be accessed via modern browsers (such as Chrome, Firefox, Safari and Edge) and with a screen resolution minimum 1280x720. The mobile app is compatible with user iOS 13.0 or higher and Android 8.0 or higher.

3.7 Assumptions and Dependencies

●      Reliable internet connectivity or cell phone signal at farm site for cloud communication.

●      Installation of IoT sensors at appropriate locations and depths are as specified in the deployment guide (Yasin et al. 2022).

●      Farmers are equipped with a smartphone or a computer that has a modern Web browser.

●      Third-party weather api services continue to be available, with the same data quality standards.

●      Cloud service providers keep their SLAs regarding high uptime, data durability.

●      The accuracy of the machine learning models for disease identification is at least 85% during validation.

3.8 Timetable

Table 7: Timeplan

(Source: Self-Developed)

4. Software Design

4.1 Design Overview

4.1.1 Sequence Diagram

The Sequence Diagram shows the interaction flow for the Automated Irrigation use case and the order of the messages exchanged between the Farmer, Mobile App, Server Backend, IoT Sensors, Database and the Irrigation System [Refer to Appendix 2]. This diagram charts the entire irrigation life cycle including initial set-up by the user, activation by sensors, and monitoring and notifying after irrigation.

4.1.2 Class Diagram

The Class Diagram shows the overall static structure of the system, including the main classes, their attributes and methods as well as the relationships between the classes [Refer to Appendix 3]. Core classes are User, Sensor, Crop, Irrigation System, Alert, Data Record, Dashboard and Weather Data. Classes and relationships have composition, aggregation and association and dependency concepts which mean that the system owns and interacts with relationships.

4.1.3 E-R Diagram

The ERD represents the organization of the data in the system, including the entities, their relationships, attribute descriptions, primary keys, and foreign keys, along with specified relationships and cardinality constraints [Refer to Appendix 4]. The main entities are User, Farm, Sensor, Crop, DataRecord, Alert, IrrigationZone, and WeatherData. The diagram is used to create a relational database (RDBMS) schema implementation using the PostgreSQL RDBMS.

4.1.4 Activity Diagram

The Activity Diagram provides an overview of the process for Crop Disease Detection, from uploading the image to processing it using AI to generate alerts and offering treatment suggestions [Refer to Appendix 5]. The decision points if there is any disease detected and the parallel activities of recording data to the database while alerting the farmer are present in the diagram.

4.2 System Architecture Design

4.2.1 Chosen System Architecture

The System Architecture Diagram illustrates the overall structure of the system and its various components, including the Presentation Layer (Web Dashboard and Mobile App), Application Layer (REST API, Authentication Service, Notification Service), Business Logic Layer (Data Processing Engine, Irrigation Control Engine, Disease Detection Engine, Predictive Analytics Engine), Data Layer (PostgreSQL, InfluxDB, File Storage), IoT Layer (IoT Gateway, Sensor Nodes, Actuators), and External Services (Weather API, AI/ML Model Service) [Refer to Appendix 6]. Data flow arrows and protocol labels describe the communication that takes place between layers.

The selected architecture is a layered and inspired architecture of Microservices, which is made up of the simplicity of layered design with advantages such as Scalability, Extension of Microservices (layers). The hybrid approach has been chosen due to the clear separation of concerns by layering while ensuring that the criteria of independently developing individual business logic components (irrigation control, disease detection, analytics) and deployment, then independently scaling them as loose micro services (Negulescu et al. 2025). The architecture enables the exchanges with the users (via the REST API) to be synchronous and the exchanges to the IoT streams (via MQTT) to be asynchronous.

4.2.2 Chosen Design Pattern

This system employs the most basic design pattern which is known as Observer Pattern. MQTT publish subscribe messaging model is used to implement Observer Pattern. In this mode of operation, the IoT sensors publish data events, while backend services listen to certain topics for new data. This decoupled communication model enables new sensors to be connected without changing the services on customer devices or new services to be added that can subscribe to data streams on new sensors without changing the sensor firmware.

MVC (Model-View-Controller) pattern is used in web dashboard and mobile application for separating the data representation (Model), rendering user interface (View), and handling user input (Controller) (Bhatti et al. 2026). Using the Repository Pattern for data access provides the abstraction between business logic and data access so one can have a clean data access interface. The Singleton Pattern makes a single instance of IoT gateway connection manager, avoiding multiple connection conflicts.

4.2.3 Alternative Design Pattern

Event Sourcing pattern along with CQRS (Command Query Responsibility Segregation) was also explored as an alternate design pattern (Jayaraman and Sharma, 2025). In this strategy, all the transitions in the state machine would be recorded as a list of immutable events, and two different read and write models will be proposed to suit each individual workload. This pattern satisfies all the other criteria and is the best way to achieve good audibility and scalability in highly complex domains, but has an amount of complexity that can at present be quite heavy for what the system requires. If the system requires more in-depth analytics, regulatory compliance auditing or temporal data access at an extended timescale, Event Sourcing might be considered for a future iteration.

4.3 User Interface Design

Figure 7: User Interface Design

(Source: Self-Developed by using Figma)

The user interface design focuses on clarity, accessibility, and responsiveness for all client applications. The web dashboard displays the overview page with all the gauges showing current data, interactive charts, locations of sensors and zone irrigation panels. Green colors set the agricultural theme and employ data visualisations to keep track of crop health. The mobile layout includes a streamlined bottom navigation roundout that lets you easily access sensors, irrigation schedule and notifications. Push sends instant alerts on moisture, disease detection results. Lastly, all touch interactive elements are handled with forty-four (44) pixel touch targets, which are very user friendly.

5. Test Plans

5.1 Features to be tested / not to be tested

The testing strategy evaluates critical features like the accuracy of sensor data acquisition and transmission, giving the data in real time, refreshing the dashboard, automatic triggering of irrigation systems, manual override of irrigation systems, the precision of the model for detecting crop diseases, user authentication and authorization, mobile application compatibility across supported devices, alert delivery and acknowledgment, and API endpoint performance under multiple load conditions. Automated unit test, integration and manual user acceptance test will be performed for each delivery.

Weather API accuracy and availability is outside the scope of this test, this is a function of third party services. Manufacturers' specifications will be used to verify the durability of hardware and physical sensor calibration in extreme environmental conditions instead of in-house testing (González Rivero et al. 2023). However, the system will be attempted with sensor networks beyond the design limits of five hundred nodes because this is a future scalability goal.

5.2 Pass/Fail Criteria

●      All critical priority test cases need to pass 100% the time

●      The total pass rate will be over 95%

●      Valid API response time must not exceed 2 seconds for the 99 percentile of all requests

●      Critical or high defect severity issues should not be outstanding on release

●      Threshold breaches activate irrigation within 5 seconds, automatically.

●      The recognition rate on the validation set needs to be better than 85%.

5.3 Approach

Establishment of evaluation method according to structured system to guarantee the reliability of the whole system. The development starts with unit tests, using Jest, pytest tools to test one piece at a time. Checks in the integration phase demonstrate the correct communication between layers include Mocha, Chai and MQTT test clients are used. Comprehensive system trials test functional requirements by supplying automatic irrigation cycles to the system. Stress Tests use JMeter, an open-source java-based load testing tool, to emulate traffic from many visitors at one time and watch the platforms' performance as the traffic dwells live. Lastly, user acceptance tests also enable local farmers to use the application, and validate absolute usability and field fitness.

5.4 Testing materials (hardware / software requirements)

Table 8: Testing materials

(Source: Self-Developed)

5.5 Test Cases

5.5.1 Test Case 1 - Automated Irrigation Trigger

Table 9: Automated Irrigation Trigger

(Source: Self-Developed)

5.5.2 Test Case 2 - Crop Disease Detection

Table 10: Crop Disease Detection

(Source: Self-Developed)

5.5.3 Test Case 3 - Dashboard Real-Time Updates

Table 11: Dashboard Real-Time Updates

(Source: Self-Developed)

5.6 Testing Results

The verification process required two weeks, including unit, integration, system, and performance trials and user acceptance tests. Forty-five test cases were run and 43 of them worked first time, with two test cases failing in the irrigation algorithm edge cases. All of these faults have been resolved with code changes which resulted in a 100% pass rate for critical and high priority items. The final result was an overall system pass rate of 97.8%. The backend API pathways validated that they yielded data processing without performance drop under user loads of as high as one hundred and fifty concurrent connections (Mabotha et al. 2025). The calculated transmission feedback were all within an acceptable 2 second server delay boundary. The evaluation was a success and is now deemed complete to enable the system to be used in the operational environment.

5.7 Conclusion

The importance of bringing network automation and data analytics together to modernise farming is clearly demonstrated by the smart farming project. The solutions presented from the structural design requirements, but also from the accurate demand reviews and the in-depth evaluations, created a secure platform for managing resources. There is a multi-tier architecture containing microservices-like components that will allow to achieve independent scalability for future growth. Sender and receiver are decoupled by using Observer pattern through MQTT protocols. In addition to that, MVC as well as Repository code design produces tidy software files, while open-source software reduces basic manufacturing costs. Automated watering helps to ensure moisture balance in the field and image classifiers detect pathogens with high accuracy in the final assessment. The user friendly layout of screens caters to the needs of different levels of agricultural expertise.

5.8 Recommendation

The system outcomes enable the software to be piloted in actual fields for three months. Testing on the two farms of different lengths and widths will bring to light important information regarding the durability of the sensors and network connectivity. Aerial cameras on drones will enable the visual tracking to be beyond just on ground locations for improving capability. The underlying database could be expanded to encompass other plant varieties, increasing the market applicability of this region (De Jonge et al. 2025). Forecasting possibilities using existing historical patterns and better planting timings through predictive analytical functionalities may be introduced. A system that incorporates new soil pH, wind speed and nutrient tracking instruments would provide a more comprehensive management system (Toselli et al. 2023). Lastly, incorporating edge computing technologies would help to reduce reliance on the cloud, making critical farming tasks more efficient and effective (Yu et al. 2025).

Appendices

Appendix 1: Smart Agricuture System - Use Case Diagram

(Source: Self-Developed by using draw.io)

Appendix 2: Sequence Diagram

(Source: Self-Developed by using draw.io)

Appendix 3: Class Diagram

(Source: Self-Developed by using draw.io)

Appendix 4: ER Diagram

(Source: Self-Developed by using draw.io)

Appendix 5: Activity Diagram

(Source: Self-Developed by using draw.io)

Appendix 6: System Architecture

(Source: Self-Developed by using draw.io)

Appendix 7: User Interface Design

(Source: https://www.figma.com/make/k7aJs1670swpv8cpMZ9hAI/Smart-Agriculture-System-UI?fullscreen=1&t=KoDm3k3Szvg1XQO9-1&code-node-id=0-9)

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