A Smartphone-Based Method for Assessing Tomato Nutrient Status through Trichome Density Measurement

• URL: http://arxiv.org/abs/2404.19513v4
• Date: Thu, 21 Nov 2024 07:39:33 GMT
• Title: A Smartphone-Based Method for Assessing Tomato Nutrient Status through Trichome Density Measurement
• Authors: Sho Ueda, Xujun Ye,
• Abstract summary: This study introduces a smartphone-based technique to quantify trichome density on young leaves with superior detection latency. A robust automated pipeline processes these images through region extraction, perspective transformation, and illumination correction to precisely quantify trichome density. This innovative approach transforms smartphones into precise diagnostic tools for plant nutrition assessment, offering a practical, cost-effective solution for precision agriculture.
• Score: 0.0
• License:
• Abstract: Early detection of fertilizer-induced stress in tomato plants is crucial for optimizing crop yield through timely management interventions. While conventional optical methods struggle to detect fertilizer stress in young leaves, these leaves contain valuable diagnostic information through their microscopic hair-like structures, particularly trichomes, which existing approaches have overlooked. This study introduces a smartphone-based noninvasive technique that leverages mobile computing and digital imaging capabilities to quantify trichome density on young leaves with superior detection latency. Our method uniquely combines augmented reality technology with image processing algorithms to analyze trichomes transferred onto specialized measurement paper. A robust automated pipeline processes these images through region extraction, perspective transformation, and illumination correction to precisely quantify trichome density. Validation experiments on hydroponically grown tomatoes under varying fertilizer conditions demonstrated the method's effectiveness. Leave-one-out cross-validation revealed strong predictive performance with the area under the precision-recall curve (PR-AUC: 0.82) and area under the receiver operating characteristic curve (ROC-AUC: 0.64), while the predicted and observed trichome densities exhibited high correlation ($r = 0.79$). This innovative approach transforms smartphones into precise diagnostic tools for plant nutrition assessment, offering a practical, cost-effective solution for precision agriculture.

Related papers

• A Novel Feature Extraction Model for the Detection of Plant Disease from Leaf Images in Low Computational Devices [2.1990652930491854]
  The proposed approach integrates various types of Deep Learning techniques to extract robust and discriminative features from leaf images. The dataset contains 10,000 leaf photos from ten classes of tomato illnesses and one class of healthy leaves. AlexNet has an accuracy score of 87%, with the benefit of being quick and lightweight, making it appropriate for use on embedded systems.
  arXiv  Detail & Related papers  (2024-10-01T19:32:45Z)
• Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD [3.285994579445155]
  This paper introduces a cutting-edge technique for the detection and classification of tomato leaf diseases. We propose a sophisticated approach within the domain of subspace learning, known as Higher-Order Whitened Singular Value Decomposition. The efficacy of this innovative method was rigorously tested through comprehensive experiments on two distinct datasets.
  arXiv  Detail & Related papers  (2024-05-30T13:46:56Z)
• Early and Accurate Detection of Tomato Leaf Diseases Using TomFormer [0.3169023552218211]
  This paper introduces a transformer-based model called TomFormer for the purpose of tomato leaf disease detection. We present a novel approach for detecting tomato leaf diseases by employing a fusion model that combines a visual transformer and a convolutional neural network.
  arXiv  Detail & Related papers  (2023-12-26T20:47:23Z)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• Generative models-based data labeling for deep networks regression: application to seed maturity estimation from UAV multispectral images [3.6868861317674524]
  Monitoring seed maturity is an increasing challenge in agriculture due to climate change and more restrictive practices. Traditional methods are based on limited sampling in the field and analysis in laboratory. We propose a method for estimating parsley seed maturity using multispectral UAV imagery, with a new approach for automatic data labeling.
  arXiv  Detail & Related papers  (2022-08-09T09:06:51Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Automated Pest Detection with DNN on the Edge for Precision Agriculture [0.0]
  This paper presents an embedded system enhanced with machine learning (ML) functionalities, ensuring continuous detection of pest infestation inside fruit orchards. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention.
  arXiv  Detail & Related papers  (2021-08-01T10:17:48Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• 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)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

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.