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Related Experiment Video

Updated: Aug 30, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

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Published on: August 5, 2020

A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming.

Navod Neranjan Thilakarathne¹, Muhammad Saifullah Abu Bakar¹, Pg Emerolylariffion Abas¹

¹Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong BE1410, Brunei.

Sensors (Basel, Switzerland)

|August 26, 2022

Summary

This summary is machine-generated.

This study introduces a cloud-based Machine Learning (ML) platform for AI-driven precision agriculture. It compares ML algorithms to recommend optimal crops, aiming to enhance farming decisions and promote open-source solutions.

Keywords:

Internet of Things IoT artificial intelligence cloud computing cop recommendation deep learning machine learning smart agriculture

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Area of Science:

Agricultural Science
Computer Science
Data Science

Background:

Modern agriculture relies on technology to meet global food demand.
Artificial Intelligence (AI) and Machine Learning (ML) are crucial for data-driven decision-making in farming.
ML systems learn from data to improve farming practices without explicit programming.

Purpose of the Study:

To provide an overview of AI-driven precision agriculture.
To propose a novel cloud-based ML-powered crop recommendation platform.
To identify the best ML algorithm for precision farming solutions.

Main Methods:

An overview of existing AI-driven precision farming research.
Development of a cloud-based ML platform for crop recommendations.
Comparative analysis of five ML algorithms: KNN, DT, RF, XGBoost, and SVM.

Main Results:

The study compares the performance of five predictive ML algorithms.
Identifies the most effective ML algorithm for the crop recommendation platform.
Highlights the potential of ML in assisting farmers with crop selection.

Conclusions:

The proposed platform aims to assist farmers in making informed crop harvesting decisions.
The research supports the development of free and open-source precision farming solutions.
Widespread adoption of such solutions can drive the growth of precision agriculture.