Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks

• URL: http://arxiv.org/abs/2105.10325v1
• Date: Fri, 21 May 2021 12:57:48 GMT
• Title: Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks
• Authors: Jana Kierdorf, Immanuel Weber, Anna Kicherer, Laura Zabawa, Lukas Drees, Ribana Roscher
• Abstract summary: The estimate of the number of berries after applying our method is closer to the manually counted reference. In contrast to applying a factor to the berry count, our approach better adapts to local conditions by directly involving the appearance of the visible berries.
• Score: 3.308833414816073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to estimate the harvest is berry counting, as it can be approached non-destructively, and its process can be automated. In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest. We use generative adversarial networks, a deep learning-based approach that generates a likely scenario behind the leaves exploiting learned patterns from images with non-occluded berries. Our experiments show that the estimate of the number of berries after applying our method is closer to the manually counted reference. In contrast to applying a factor to the berry count, our approach better adapts to local conditions by directly involving the appearance of the visible berries. Furthermore, we show that our approach can identify which areas in the image should be changed by adding new berries without explicitly requiring information about hidden areas.

Related papers

• Few-Shot Fruit Segmentation via Transfer Learning [4.616529139444651]
  We develop a few-shot semantic segmentation framework for infield fruits using transfer learning. Motivated by similar success in urban scene parsing, we propose specialized pre-training. We show that models with pre-training learn to distinguish between fruit still on the trees and fruit that have fallen on the ground.
  arXiv  Detail & Related papers  (2024-05-04T04:05:59Z)
• LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion [53.98099367578345]
  We propose a Latent Background Knowledge Retrieval-Augmented Diffusion (LAKE-RED) for camouflaged image generation. Our method is the first knowledge retrieval-augmented method with interpretability for camouflaged generation. Experimental results demonstrate that our method outperforms the existing approaches.
  arXiv  Detail & Related papers  (2024-03-30T08:51:23Z)
• Vision-Based Cranberry Crop Ripening Assessment [1.8434042562191815]
  This work is the first of its kind in quantitative evaluation of ripening using computer vision methods. It has impact beyond cranberry crops including wine grapes, olives, blueberries, and maize.
  arXiv  Detail & Related papers  (2023-08-31T14:58:11Z)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference [54.17205151960878]
  We introduce a sampling-free approach that is generic and easy to deploy. We produce reliable uncertainty estimates on par with state-of-the-art methods at a significantly lower computational cost.
  arXiv  Detail & Related papers  (2022-11-21T13:23:09Z)
• 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)
• 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)
• AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk [3.8902094267855163]
  This paper develops an end-to-end cranberry health monitoring system to enable and support real time cranberry over-heating assessment. Drone-based field data and ground-based sky data collection systems are used to collect video imagery at multiple time points for use in crop health analysis. The sun irradiance prediction error was found to be 8.41-20.36% MAPE in the 5-20 minutes time horizon.
  arXiv  Detail & Related papers  (2020-11-08T20:03:20Z)
• High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks [23.67862313758282]
  We propose a deep learning based approach, named DeepStand, for image-based corn stand counting at early phenological stages. Our proposed method can successfully count corn stands and out-perform other state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-23T17:28:29Z)
• Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors [3.6704226968275258]
  We present a deep learning method for simultaneous segmentation and counting of cranberries to aid in yield estimation and sun exposure predictions. The approach, named Triple-S Network, incorporates a three-part loss with shape priors to promote better fitting to objects of known shape typical in agricultural scenes. Our results improve overall segmentation performance by more than 6.74% and counting results by 22.91% when compared to state-of-the-art.
  arXiv  Detail & Related papers  (2020-04-18T01:08:57Z)

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.