Empowering
MMSU Farmers: Smart Decisions, Bountiful Harvest

A Thesis Research Study on Enhancing Precision Agriculture at
MMSU i4.0: A Comparative Analysis of Machine Learning Algorithm
and Hybrid Algorithm For Crop Calendar Prediction

Web App
Utility
and
Features

CROP CALENDAR PREDICTION

Our main objective is to develop a crop calendar prediction system using two algorithms - machine learning and hybrid algorithm(genetic algorithm and simulated annealing. This functions as a decision support tool to aide farmers in crop scheduling and resource utilization

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GRAPH VISUALIZATION

Graph Visualizations simplify the process of identifying trends and how soil and certain crops respond to a specific combinations of fertilizer applications particularly Nitrogen, Phosphorus and Potassium application as well as detecting soil conditions

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CUSTOM INTERVAL CONTROL

Sensor nodes operate at a given interval from the app, authorized users can set a custom time interval to control directly the hardwares' sensor readings for realtime field monitoring.

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CUSTOM PREDICTIONS PARAMETER BASED

Along with rice and corn the user can input custom parameters based on their domain knowledge to identify the optimal periods for crops.This helps in predicting the best time frame for any particular crops planted locally.

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Field Constraints
and Resource
monitoring

Climatic Data
from Local PAG-ASA
Agrometeorological
Station

Related Information

01
Hybrid Algorithm
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02
Machine Learning
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03
Hardware Design
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04
Software Walkthrough
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05
Rice Crop Calendar
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06
Corn Crop Calendar
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