Accurate Crop Yield Estimation of Blueberries using Deep Learning and Smart Drones

• URL: http://arxiv.org/abs/2501.02344v1
• Date: Sat, 04 Jan 2025 17:45:29 GMT
• Title: Accurate Crop Yield Estimation of Blueberries using Deep Learning and Smart Drones
• Authors: Hieu D. Nguyen, Brandon McHenry, Thanh Nguyen, Harper Zappone, Anthony Thompson, Chau Tran, Anthony Segrest, Luke Tonon,
• Abstract summary: We present an AI pipeline that involves using smart drones equipped with computer vision to obtain a more accurate fruit count and yield estimation. The core components are two object-detection models based on the YOLO deep learning architecture. We describe how to deploy our models to map out blueberry fields using different sampling strategies.
• Score: 5.586392657005597
• License:
• Abstract: We present an AI pipeline that involves using smart drones equipped with computer vision to obtain a more accurate fruit count and yield estimation of the number of blueberries in a field. The core components are two object-detection models based on the YOLO deep learning architecture: a Bush Model that is able to detect blueberry bushes from images captured at low altitudes and at different angles, and a Berry Model that can detect individual berries that are visible on a bush. Together, both models allow for more accurate crop yield estimation by allowing intelligent control of the drone's position and camera to safely capture side-view images of bushes up close. In addition to providing experimental results for our models, which show good accuracy in terms of precision and recall when captured images are cropped around the foreground center bush, we also describe how to deploy our models to map out blueberry fields using different sampling strategies, and discuss the challenges of annotating very small objects (blueberries) and difficulties in evaluating the effectiveness of our models.

Related papers

• Plantation Monitoring Using Drone Images: A Dataset and Performance Review [2.4936576553283287]
  Small, low cost drones equipped with an RGB camera can capture high-resolution images of agricultural fields. Existing methods of automated plantation monitoring are mostly based on satellite images. We propose an automated system for plantation health monitoring using drone images.
  arXiv  Detail & Related papers  (2025-02-12T09:21:16Z)
• Coconut Palm Tree Counting on Drone Images with Deep Object Detection and Synthetic Training Data [5.492715335713603]
  This study utilized YOLO, a real-time object detector, to identify and count coconut palm trees in Ghanaian farm drone footage. To optimize YOLO with scarce data, synthetic images were created for model training and validation.
  arXiv  Detail & Related papers  (2024-12-16T16:33:28Z)
• Agtech Framework for Cranberry-Ripening Analysis Using Vision Foundation Models [1.5728609542259502]
  We develop a framework for characterizing the ripening process of cranberry crops using aerial and ground imaging. This work is the first of its kind and has future impact for cranberries and for other crops including wine grapes, olives, blueberries, and maize.
  arXiv  Detail & Related papers  (2024-12-12T22:03:33Z)
• 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)
• ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering [83.75284107397003]
  We introduce ScatterNeRF, a neural rendering method which renders scenes and decomposes the fog-free background. We propose a disentangled representation for the scattering volume and the scene objects, and learn the scene reconstruction with physics-inspired losses. We validate our method by capturing multi-view In-the-Wild data and controlled captures in a large-scale fog chamber.
  arXiv  Detail & Related papers  (2023-05-03T13:24:06Z)
• Robustifying the Multi-Scale Representation of Neural Radiance Fields [86.69338893753886]
  We present a robust multi-scale neural radiance fields representation approach to overcome both real-world imaging issues. Our method handles multi-scale imaging effects and camera-pose estimation problems with NeRF-inspired approaches. We demonstrate, with examples, that for an accurate neural representation of an object from day-to-day acquired multi-view images, it is crucial to have precise camera-pose estimates.
  arXiv  Detail & Related papers  (2022-10-09T11:46:45Z)
• 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)
• SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation [101.55622133406446]
  We propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets.
  arXiv  Detail & Related papers  (2022-04-07T17:58:47Z)
• 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)
• Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks [3.308833414816073]
  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.
  arXiv  Detail & Related papers  (2021-05-21T12:57:48Z)
• Wide-angle Image Rectification: A Survey [86.36118799330802]
  wide-angle images contain distortions that violate the assumptions underlying pinhole camera models. Image rectification, which aims to correct these distortions, can solve these problems. We present a detailed description and discussion of the camera models used in different approaches. Next, we review both traditional geometry-based image rectification methods and deep learning-based methods.
  arXiv  Detail & Related papers  (2020-10-30T17:28:40Z)

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.