Few-Shot Fruit Segmentation via Transfer Learning
- URL: http://arxiv.org/abs/2405.02556v1
- Date: Sat, 4 May 2024 04:05:59 GMT
- Title: Few-Shot Fruit Segmentation via Transfer Learning
- Authors: Jordan A. James, Heather K. Manching, Amanda M. Hulse-Kemp, William J. Beksi,
- Abstract summary: 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.
- Score: 4.616529139444651
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Advancements in machine learning, computer vision, and robotics have paved the way for transformative solutions in various domains, particularly in agriculture. For example, accurate identification and segmentation of fruits from field images plays a crucial role in automating jobs such as harvesting, disease detection, and yield estimation. However, achieving robust and precise infield fruit segmentation remains a challenging task since large amounts of labeled data are required to handle variations in fruit size, shape, color, and occlusion. In this paper, we develop a few-shot semantic segmentation framework for infield fruits using transfer learning. Concretely, our work is aimed at addressing agricultural domains that lack publicly available labeled data. Motivated by similar success in urban scene parsing, we propose specialized pre-training using a public benchmark dataset for fruit transfer learning. By leveraging pre-trained neural networks, accurate semantic segmentation of fruit in the field is achieved with only a few labeled images. Furthermore, we show that models with pre-training learn to distinguish between fruit still on the trees and fruit that have fallen on the ground, and they can effectively transfer the knowledge to the target fruit dataset.
Related papers
- MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models [10.11552909915055]
We introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances.
This study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection.
arXiv Detail & Related papers (2024-05-14T00:13:47Z) - A pipeline for multiple orange detection and tracking with 3-D fruit
relocalization and neural-net based yield regression in commercial citrus
orchards [0.0]
We propose a non-invasive alternative that utilizes fruit counting from videos, implemented as a pipeline.
To handle occluded and re-appeared fruit, we introduce a relocalization component that employs 3-D estimation of fruit locations.
By ensuring that at least 30% of the fruit is accurately detected, tracked, and counted, our yield regressor achieves an impressive coefficient of determination of 0.85.
arXiv Detail & Related papers (2023-12-27T21:22:43Z) - Transferring Knowledge for Food Image Segmentation using Transformers
and Convolutions [65.50975507723827]
Food image segmentation is an important task that has ubiquitous applications, such as estimating the nutritional value of a plate of food.
One challenge is that food items can overlap and mix, making them difficult to distinguish.
Two models are trained and compared, one based on convolutional neural networks and the other on Bidirectional representation for Image Transformers (BEiT)
The BEiT model outperforms the previous state-of-the-art model by achieving a mean intersection over union of 49.4 on FoodSeg103.
arXiv Detail & Related papers (2023-06-15T15:38:10Z) - Fruit Ripeness Classification: a Survey [59.11160990637616]
Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded.
Machine learning and deep learning techniques dominate the top-performing methods.
Deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features.
arXiv Detail & Related papers (2022-12-29T19:32:20Z) - 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) - Facilitated machine learning for image-based fruit quality assessment in
developing countries [68.8204255655161]
Automated image classification is a common task for supervised machine learning in food science.
We propose an alternative method based on pre-trained vision transformers (ViTs)
It can be easily implemented with limited resources on a standard device.
arXiv Detail & Related papers (2022-07-10T19:52:20Z) - Enlisting 3D Crop Models and GANs for More Data Efficient and
Generalizable Fruit Detection [0.0]
We propose a method that generates agricultural images from a synthetic 3D crop model domain into real world crop domains.
The method uses a semantically constrained GAN (generative adversarial network) to preserve the fruit position and geometry.
Incremental training experiments in vineyard grape detection tasks show that the images generated from our method can significantly speed the domain process.
arXiv Detail & Related papers (2021-08-30T16:11:59Z) - Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep
Learning [14.853897011640022]
We present a system to measure the ripeness of fruit with a hyperspectral camera and a suitable deep neural network architecture.
This architecture did outperform competitive baseline models on the prediction of the state of ripeness.
arXiv Detail & Related papers (2021-04-20T07:43:19Z) - Agriculture-Vision: A Large Aerial Image Database for Agricultural
Pattern Analysis [110.30849704592592]
We present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns.
Each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel.
We annotate nine types of field anomaly patterns that are most important to farmers.
arXiv Detail & Related papers (2020-01-05T20:19:33Z)
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.