TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models

• URL: http://arxiv.org/abs/2307.01064v1
• Date: Mon, 3 Jul 2023 14:43:40 GMT
• Title: TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models
• Authors: Marija Ivanovska, Vitomir Struc, Janez Pers
• Abstract summary: TomatoDIFF is a novel diffusion-based model for semantic segmentation of on-plant tomatoes. Tomatopia is a new, large and challenging dataset of greenhouse tomatoes.
• Score: 3.597418929000278
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Artificial intelligence applications enable farmers to optimize crop growth and production while reducing costs and environmental impact. Computer vision-based algorithms in particular, are commonly used for fruit segmentation, enabling in-depth analysis of the harvest quality and accurate yield estimation. In this paper, we propose TomatoDIFF, a novel diffusion-based model for semantic segmentation of on-plant tomatoes. When evaluated against other competitive methods, our model demonstrates state-of-the-art (SOTA) performance, even in challenging environments with highly occluded fruits. Additionally, we introduce Tomatopia, a new, large and challenging dataset of greenhouse tomatoes. The dataset comprises high-resolution RGB-D images and pixel-level annotations of the fruits.

Related papers

• 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)
• 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)
• Tomato Maturity Recognition with Convolutional Transformers [5.220581005698766]
  Authors propose a novel method for tomato maturity classification using a convolutional transformer. New tomato dataset named KUTomaData is designed to train deep-learning models for tomato segmentation and classification. Authors show that the convolutional transformer outperforms state-of-the-art methods for tomato maturity classification.
  arXiv  Detail & Related papers  (2023-07-04T07:33:53Z)
• Early Detection of Late Blight Tomato Disease using Histogram Oriented Gradient based Support Vector Machine [2.3210922904864955]
  This research work propose a novel smart technique for early detection of late blight diseases in tomatoes. The proposed hybrid algorithm of SVM and HOG has significant potential for the early detection of late blight disease in tomato plants.
  arXiv  Detail & Related papers  (2023-06-14T07:58:14Z)
• Visual based Tomato Size Measurement System for an Indoor Farming Environment [3.176607626141415]
  This paper presents a size measurement method combining a machine learning model and depth images captured from three low cost RGBD cameras. The performance of the presented system is evaluated on a lab environment with real tomato fruits and fake leaves. Our three-camera system was able to achieve a height measurement accuracy of 0.9114 and a width accuracy of 0.9443.
  arXiv  Detail & Related papers  (2023-04-12T22:27:05Z)
• Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery [101.18253437732933]
  We present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery. We solve real-world deep learning problems in the very specific context of agave crop segmentation. With the resulting accurate models, agave production forecasting can be made available for large regions.
  arXiv  Detail & Related papers  (2023-03-21T03:15:29Z)
• Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
  In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
  arXiv  Detail & Related papers  (2022-10-24T14:37:09Z)
• End-to-end deep learning for directly estimating grape yield from ground-based imagery [53.086864957064876]
  This study demonstrates the application of proximal imaging combined with deep learning for yield estimation in vineyards. Three model architectures were tested: object detection, CNN regression, and transformer models. The study showed the applicability of proximal imaging and deep learning for prediction of grapevine yield on a large scale.
  arXiv  Detail & Related papers  (2022-08-04T01:34:46Z)
• Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review [5.639656362091594]
  generative adversarial network (GAN) invented in 2014 in the computer vision community, provides suite of novel approaches that can learn good data representations. This paper presents an overview of the evolution of GAN architectures followed by a systematic review of their application to agriculture.
  arXiv  Detail & Related papers  (2022-04-10T15:33:05Z)
• 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)
• 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.