Last modified on May 1st, 2021
Neural Network-based Intelligent Irrigation Application –
Project Code Documentation
Project Domain / Category
Artificial Intelligence /Mobile App
Abstract / Introduction.
Irrigation is a significant factor in determining the cotton crop yield which varies with the geographical, climatic and topological factors. Farmers primarily depend on personal monitoring and their personal experience in irrigating the fields resulted inefficient and irregular irrigation. This calls for the need of the system which can provide an efficient and deployable solution. Different tools have been developed with the aim of improving irrigation scheduling. These tools have the potential to aid farmers in conserving water and nutrients, while maintaining crop yields.
The water usages of cotton crop cultivated on area of one acre (43,560 square feet) or 69.50 yard is approximately 325,850 gallons where each plant consumes 10 gallons of water in its whole life till the harvesting moderate climate condition.
You are needed to develop an artificial intelligence (AI) based mobile application which internally implementing an artificial neural networks (Prediction algorithm) automatically. The capacity of the irrigation system should be able to provide enough water, in terms of quantity, time, and frequency, to the crop, whenever this demand is the highest, so water application is ensured during the entire crop cycle.
This modern intelligent application which schedule the irrigation of crops is considered as supervised learning (AI) based approach, accepts input in different types of parameters named as area of land in sqr feet, quantity of water in gallons, of temperature in Celsius unit, of time in minutes or hours the land is needed to irrigate. The farmers, by putting the values of different inputs parameters mentioned above can irrigate their crops in efficient way by getting output accurately. This application would inform the farmers to switch off the irrigation system on reaching the moderate output level.
Application must have two graphical user interface (GUI) to register and login to the application with the authentic user name and password. Admin should manage all activities of input and output parameters on the GUI interface.
You need to perform following tasks while developing an intelligent application based on artificial neural network (supervised learning).
- Define the problem( User should firstly select one specific crop)
- Define the Predicting algorithm
- Define the IDE for training of the algorithm
- Define Linguistic Variables
- Define the developing software
- Follow all steps of the training process(learning rules)
- Train algorithm by performing all required steps
- Test algorithm
- Tune the Algorithm
- The application must use a knowledge-based system with the predicting algorithm (artificial neural network) (Specifically select a crop then build knowledge according to that crop)
- The admin must manage reports weekly basis.
- The admin must manage and view all backup records.
- The admin should view the performance of crops weekly basis and modify knowledge-based accordingly.
Tools: Python or Java language, MatLab
Prerequisite: Artificial Neural Networks
class diagram, activity diagram, data flow diagram, sequence diagram, use case diagram, testing test cases, SRS document and others are need to draw for this project.
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