Multimodal Data Integration for Sustainable Indoor Gardening: Tracking Anyplant with Time Series Foundation Model

• URL: http://arxiv.org/abs/2503.21932v1
• Date: Thu, 27 Mar 2025 19:19:37 GMT
• Title: Multimodal Data Integration for Sustainable Indoor Gardening: Tracking Anyplant with Time Series Foundation Model
• Authors: Seyed Hamidreza Nabaei, Zeyang Zheng, Dong Chen, Arsalan Heydarian,
• Abstract summary: This paper presents a novel framework that integrates computer vision, machine learning (ML), and environmental sensing for the automated monitoring of plant health and growth.<n>Unlike previous approaches, this framework combines RGB imagery, plant phenotyping data, and environmental factors such as temperature and humidity, to predict plant water stress in a controlled growth environment.
• Score: 9.8186542545443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Indoor gardening within sustainable buildings offers a transformative solution to urban food security and environmental sustainability. By 2030, urban farming, including Controlled Environment Agriculture (CEA) and vertical farming, is expected to grow at a compound annual growth rate (CAGR) of 13.2% from 2024 to 2030, according to market reports. This growth is fueled by advancements in Internet of Things (IoT) technologies, sustainable innovations such as smart growing systems, and the rising interest in green interior design. This paper presents a novel framework that integrates computer vision, machine learning (ML), and environmental sensing for the automated monitoring of plant health and growth. Unlike previous approaches, this framework combines RGB imagery, plant phenotyping data, and environmental factors such as temperature and humidity, to predict plant water stress in a controlled growth environment. The system utilizes high-resolution cameras to extract phenotypic features, such as RGB, plant area, height, and width while employing the Lag-Llama time series model to analyze and predict water stress. Experimental results demonstrate that integrating RGB, size ratios, and environmental data significantly enhances predictive accuracy, with the Fine-tuned model achieving the lowest errors (MSE = 0.420777, MAE = 0.595428) and reduced uncertainty. These findings highlight the potential of multimodal data and intelligent systems to automate plant care, optimize resource consumption, and align indoor gardening with sustainable building management practices, paving the way for resilient, green urban spaces.

Related papers

• Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning [3.057175662139921]
  We deployed IoT sensors in strawberry production polytunnels for two growing seasons to collect environmental data. The sensor observations were combined with manually provided yield records spanning a period of four seasons. We built an AI-based yield forecasting model to evaluate our approach using the combination of real and synthetic observations.
  arXiv  Detail & Related papers  (2025-04-25T16:02:50Z)
• Sustainable Greenhouse Microclimate Modeling: A Comparative Analysis of Recurrent and Graph Neural Networks [0.0]
  This study introduces a novel application of Spatio-Temporal Graph Neural Networks (STGNNs) to greenhouse microclimate modeling.<n>Using high-frequency data collected at 15-minute intervals from a greenhouse in Volos, Greece, we demonstrate that RNNs achieve exceptional accuracy in winter conditions.
  arXiv  Detail & Related papers  (2025-02-24T17:52:01Z)
• A Dataset and Benchmark for Shape Completion of Fruits for Agricultural Robotics [30.46518628656399]
  We propose the first publicly available 3D shape completion dataset for agricultural vision systems.<n>We provide an RGB-D dataset for estimating the 3D shape of fruits.
  arXiv  Detail & Related papers  (2024-07-18T09:07:23Z)
• Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
  This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
  arXiv  Detail & Related papers  (2024-03-27T08:42:47Z)
• BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions [30.27773980916216]
  Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability. Advancements in field management through non-chemical weeding by robots in combination with monitoring of crops by autonomous unmanned aerial vehicles (UAVs) are helpful to address these challenges. The analysis of plant traits, called phenotyping, is an essential activity in plant breeding, it however involves a great amount of manual labor.
  arXiv  Detail & Related papers  (2023-12-22T14:06:44Z)
• FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems [28.166089112650926]
  FREE maps available environmental data into a text space and then converts the traditional predictive modeling task in environmental science to a semantic recognition problem. When used for long-term prediction, FREE has the flexibility to incorporate newly collected observations to enhance future prediction. Free is evaluated in the context of two societally important real-world applications, predicting stream water temperature in the Delaware River Basin and predicting annual corn yield in Illinois and Iowa.
  arXiv  Detail & Related papers  (2023-11-17T00:53:09Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• AI driven shadow model detection in agropv farms [0.0]
  Agro-photovoltaic (APV) is a growing farming practice that combines agriculture and solar photovoltaic projects within the same area. Identifying shadows is crucial to understanding the APV environment, as they impact plant growth, microclimate, and evapotranspiration. We use state-of-the-art CNN and GAN-based neural networks to detect shadows in agro-PV farms, demonstrating their effectiveness.
  arXiv  Detail & Related papers  (2023-04-16T18:33:20Z)
• A Learned Simulation Environment to Model Plant Growth in Indoor Farming [0.0]
  We developed a simulator to quantify the effect of changes in environmental parameters on plant growth in precision farming. Our approach combines the processing of plant images with deep convolutional neural networks (CNN), growth curve modeling, and machine learning.
  arXiv  Detail & Related papers  (2022-12-06T17:28:13Z)
• Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks [1.2926587870771542]
  The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high- throughput imaging sensor measurements and their automatic analysis. Our approach's core is a novel machine learning-based growth model based on conditional generative adversarial networks.
  arXiv  Detail & Related papers  (2021-05-17T13:00:01Z)
• Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
  We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity. Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
  arXiv  Detail & Related papers  (2020-12-07T16:23:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.