# Machine Learning Solution for Predicting Vertical Farming Cube Hatch Status Using Temperature and Humidity Sensors

> This article introduces a practical project applying machine learning to vertical farming environments, predicting the open/closed status of planting hatch doors using temperature and humidity sensor data, and comparing the performance of three algorithms: Random Forest, Support Vector Machine (SVM), and XGBoost.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-01T00:46:08.000Z
- 最近活动: 2026-06-01T00:48:09.117Z
- 热度: 144.0
- 关键词: 垂直农业, 机器学习, 传感器数据, 分类模型, 支持向量机, 随机森林, XGBoost, 智能农业, 物联网
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-sam5121472-vertical-farming-door-prediction
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-sam5121472-vertical-farming-door-prediction
- Markdown 来源: floors_fallback

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## Introduction: Machine Learning Solution for Predicting Vertical Farming Hatch Status Using Temperature and Humidity Sensors

This project aims to predict the open/closed status of vertical farming cube hatch doors using temperature and humidity sensor data, and compare the performance of three algorithms: Random Forest, Support Vector Machine (SVM), and XGBoost. The results show that SVM performs best with an accuracy of 99.36%, providing technical support for the intelligence of vertical farming.

## Project Background and Core Objectives

Vertical farming has high requirements for environmental stability, and the hatch status affects temperature and humidity balance as well as energy consumption. Traditional manual inspection is inefficient. The goal of this project is to build a machine learning classification model to automatically predict the open/closed status of hatches, supporting intelligent agricultural applications such as automated control and anomaly detection.

## Dataset and Model Training Methods

The dataset contains 32,000 records. Input features include Cube ID, temperature and humidity of layers A/B, and timestamp. The target variable is the hatch status (0 for closed / 1 for open). Feature engineering steps: convert timestamp to Unix format, apply Min-Max normalization to numerical features. EDA analysis confirms that there are learnable patterns between temperature/humidity and hatch status. The models selected are Random Forest, SVM, and XGBoost, and the dataset is split into training/test sets in an 8:2 ratio.

## Model Performance Evaluation Results

Performance comparison of three models:
| Model | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|
| Random Forest | 99.20% | 99.12% | 99.27% | 99.20% |
| SVM | 99.36% | 99.37% | 99.34% | 99.35% |
| XGBoost | 99.23% | 99.24% | 99.21% | 99.23% |
SVM has the best overall performance. Confusion matrix analysis shows that the model performs balanced in predicting both classes.

## Application Value and Future Expansion Directions

Application Value: Automated hatch control, energy consumption optimization, anomaly detection, intelligent agriculture integration. Future Expansion: Introduce light/CO₂ sensors, combine with time-series models, explore the performance of deep learning on large-scale data.

## Project Summary

This project completes a full machine learning workflow from data preprocessing to multi-model comparison, building a hatch status prediction model with an accuracy of over 99%, where SVM performs best. This solution provides a feasible path for the intelligent upgrade of vertical farming and demonstrates the application potential of machine learning in the traditional agricultural field.