Data Analysis and Decision-Making in Intelligent Greenhouses Using Machine Learning

EasyChair Preprint 13224

Abstract

Intelligent greenhouses have emerged as a promising solution to enhance agricultural productivity and sustainability. These advanced systems leverage various sensors and monitoring devices to collect vast amounts of data related to environmental conditions and plant growth. However, making sense of this data and extracting actionable insights pose significant challenges. This abstract provides an overview of the role of data analysis and decision-making in intelligent greenhouses, with a specific focus on the application of machine learning techniques.

Data analysis in intelligent greenhouses involves the collection, preprocessing, and analysis of diverse data types, including environmental parameters (such as temperature, humidity, light intensity, and CO2 levels) and plant-related variables (such as growth rate, nutrient levels, and disease symptoms). Preprocessing techniques are applied to clean and transform the data, addressing issues such as missing values, outliers, and normalization. Feature selection methods help identify the most relevant variables for analysis.

Machine learning algorithms play a crucial role in extracting meaningful insights from greenhouse data. Supervised learning algorithms, including regression and classification models, enable yield prediction and disease detection, respectively. Unsupervised learning algorithms, such as clustering and anomaly detection, assist in identifying plant groups and detecting unusual patterns. Reinforcement learning techniques contribute to autonomous control and optimization in intelligent greenhouses.

Keyphrases: Challenges, Integration, data analysis, decision making

@booklet{EasyChair:13224,
  author    = {Ayuns Luz and Godwin Olaoye},
  title     = {Data Analysis and Decision-Making in Intelligent Greenhouses Using Machine Learning},
  howpublished = {EasyChair Preprint 13224},
  year      = {EasyChair, 2024}}