Exploring a cost-effective way for nutrient management with machine learning for container plants

Aug 4, 2026

Upload by Rowan

Timely references of plant nutrient conditions are crucial for container plant production to improve fertilizer use efficiency and reduce environmental waste. The work evaluates greenhouse container crops (basil, marigold, pepper, and sage) under five nitrogen-based fertilization input rates and tracks chlorophyll content, growth index, canopy area, and biomass. Because canopy color values closely relate to growth, an RGB imaging workflow using ImageJ and supervised machine learning classifies fertilization status into four levels. Trained models using K-Nearest Neighbor, Support Vector Machine, and Naïve Bayes achieve 0.7–1.0 accuracy for guiding fertilizer schedules during vegetative growth.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Abstract

Introduction

Objective and monitoring approach

Imaging and machine learning workflow

Classification of fertilization input levels

Model training and evaluation

Application for precision nutrient management

ARTICLE | Open Access [https://doi.org/10.48130/tihort-0025-0007](https://doi.org/10.48130/tihort-0025-0007)
Technology in Horticulture 2025, 5: e012
Exploring a cost-effective way for nutrient management with machine learning for container plants
Ping Yu\#* and Kuan Qin\#
Department of Horticulture, University of Georgia, Griffin, GA 30223, USA
\# Authors contributed equally: Ping Yu, Kuan Qin
* Corresponding author, [E-mail: pingyu@uga.edu](E-mail: pingyu@uga.edu) | | --- | | Abstract
Providing timely references of plant nutrient conditions is essential for container plant production with improved fertilizer use efficiency and reduced environmental waste. Imaging systems, as an effective real-time plant monitoring (e.g., canopy area, leaf color change) approach, are less tested in container crops. We imposed four types of greenhouse container plants (basil, marigold, pepper, and sage) with five fertilization input rates based on nitrogen, and found that plant chlorophyll content, growth index, canopy area, and biomass were gradually increased along with increased fertilizer rates. Due to the close relationships between plant canopy color values and growth parameters, an RGB imaging system using ImageJ to process the image analysis with a supervised machine learning approach was used to classify fertilization input status with four levels: extreme-underuse, sub-underuse, sufficient, and overuse. By training the data using K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes algorithms, this system could reach up to 0.7–1.0 accuracy in guiding fertilization schedules during the plant vegetative growth stage. This imaging system with machine learning models offered a cost-effective, real-time monitoring, and decision-making control platform for precision nutrient management for greenhouse plants. | | Citation: Yu P, Qin K. 2025. Exploring a cost-effective way for nutrient management with machine learning for container plants. Technology in Horticulture 5: e012
[https://doi.org/10.48130/tihort-0025-0007](https://doi.org/10.48130/tihort-0025-0007) | Introduction Container plant production for indoor and patio uses, including ornamental plants, vegetables, and herbs, has become an important segment of green industry. Precision irrigation and fertilization schedules for container crops have been developed and widely used for nursery stocks and gardening[1] . However, several problems related to nutrient deficiency and excessiveness due to the improper use of water-soluble fertilizer, failed rate control of slow-release fertilizer, and untimely and abrupt heavy irrigation, could lead to significant reductions in plant health with lower foliar and floral qualities, causing economic waste, eutrophication, and environmental pollution[2] . Over the past century, a tremendous amount of fertilizer, especially anthropogenic nitrogen (N) fertilizer, in the amount of 100 TgN·yr−1, has been applied to agricultural systems[3,4] . Considering plants can only absorb 35% of the applied fertilizer, even under ideal conditions, and N fertilizer costs experienced significant inflation in the past 5 years by increasing 150%[5], an improved fertilizer use efficiency with precise fertilization scheduling is desired for container plant production. To provide timely references for restoring or cutting-off fertilizer supply, it is essential to monitor and track changes in plant nutrient conditions, which could provide insights into the current status of fertilization input rate. Direct accurate and timely measurements of plant nutrient concentrations require time-consuming and labor-intensive sample collection and lab analysis, which is normally destructive and sitespecific for the plant. The direct relationships between leaf chlorophyll content and tissue N concentration, along with the absorption or reflectance of different light wavelengths (e.g., red, blue) due to the presence of chlorophyll, has fo

FAQs

Why is nutrient management challenging in container plant production?

Nutrient deficiency or excess can result from improper fertilizer use and irrigation timing, reducing plant health and causing economic waste and environmental pollution. Direct nutrient measurements also require labor-intensive and often destructive sampling.

How does the study monitor nutrient status using imaging?

It uses an RGB imaging system and processes images with ImageJ to extract canopy color information. These values are then used to infer plant growth-related nutrient conditions.

Which machine learning models are used and how accurate is the system?

The workflow trains supervised models including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes. The system reaches up to 0.7–1.0 accuracy for guiding fertilization schedules during vegetative growth.

Aug 4, 2026

Upload by Rowan