Articles
Crop growth monitoring with time series data based on deep learning
Article number
1426_4
Pages
23 – 30
Language
English
Abstract
This study presents a model based on deep learning to analyze the interaction of variables that influence plant growth in greenhouse crops.
To achieve this, a multi-category database has been collected in two types of open and semi-open greenhouses throughout the year during three growing seasons, including two cultivars of tomato plants with a sample of 100 plants per season.
The database consists of images captured at the growth point of the plants, phenotyping data obtained weekly, and environmental data with a sampling frequency per minute.
The implemented model uses time series data to find the relationship of variables and automatically predict the state of the crop at the individual plant level and further extended as a group of plants.
In summary, the proposed research shows a practical solution to monitor plants over time and provides strategies for multi-category data collection that allow a better understanding of the dynamics of plant stress factors.
To achieve this, a multi-category database has been collected in two types of open and semi-open greenhouses throughout the year during three growing seasons, including two cultivars of tomato plants with a sample of 100 plants per season.
The database consists of images captured at the growth point of the plants, phenotyping data obtained weekly, and environmental data with a sampling frequency per minute.
The implemented model uses time series data to find the relationship of variables and automatically predict the state of the crop at the individual plant level and further extended as a group of plants.
In summary, the proposed research shows a practical solution to monitor plants over time and provides strategies for multi-category data collection that allow a better understanding of the dynamics of plant stress factors.
Publication
Authors
A. Fuentes, J. Dong, J. Lee, T. Kim, S. Yoon, D.S. Park
Keywords
deep learning, plant growth, tomato plant, smart agriculture, data, sensors
Groups involved
- Division Precision Horticulture and Engineering
- Division Greenhouse and Indoor Production Horticulture
- Working Group Nettings in Horticulture (subgroup of Protected Cultivation in Mild Winter Climates)
- Working Group Light in Horticulture
- Working Group Organic Greenhouse Horticulture
- Working Group Vegetable Grafting
- Working Group Modelling Plant Growth, Environmental Control, Greenhouse Environment
- Working Group Protected Cultivation, Nettings and Screens for Mild Climates
- Working Group Computational Fluid Dynamics in Agriculture
- Working Group Design and Automation in Integrated Indoor Production Systems
- Working Group Mechanization, Digitization, Sensing and Robotics
- Working Group Greenhouse Environment and Climate Control
- Division Landscape and Urban Horticulture
- Commission Agroecology and Organic Farming Systems
- Division Vegetables, Roots and Tubers
- Working Group Vertical Farming
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