Apple Defect Detection Using Deep Learning Based Object Detection For Better Post Harvest Handling

• URL: http://arxiv.org/abs/2005.06089v1
• Date: Tue, 12 May 2020 23:34:43 GMT
• Title: Apple Defect Detection Using Deep Learning Based Object Detection For Better Post Harvest Handling
• Authors: Paolo Valdez
• Abstract summary: apples are susceptible to a wide range of defects that can occur during harvesting or during the post-harvesting period. Recent computer vision and deep learning methods can help in detecting healthy apples from apples with defects.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The inclusion of Computer Vision and Deep Learning technologies in Agriculture aims to increase the harvest quality, and productivity of farmers. During postharvest, the export market and quality evaluation are affected by assorting of fruits and vegetables. In particular, apples are susceptible to a wide range of defects that can occur during harvesting or/and during the post-harvesting period. This paper aims to help farmers with post-harvest handling by exploring if recent computer vision and deep learning methods such as the YOLOv3 (Redmon & Farhadi (2018)) can help in detecting healthy apples from apples with defects.

Related papers

• Precise Apple Detection and Localization in Orchards using YOLOv5 for Robotic Harvesting Systems [0.0]
  We propose a novel approach to apple detection and position estimation utilizing an object detection model, YOLOv5. Our results demonstrate that the YOLOv5 model outperforms its counterparts, achieving an impressive apple detection accuracy of approximately 85%.
  arXiv  Detail & Related papers  (2024-05-10T06:17:00Z)
• 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)
• Revolutionizing Agrifood Systems with Artificial Intelligence: A Survey [93.34268594812599]
  We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery. We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
  arXiv  Detail & Related papers  (2023-05-03T05:16:54Z)
• 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)
• An Ensemble of Convolutional Neural Networks to Detect Foliar Diseases in Apple Plants [0.0]
  Apple diseases, if not diagnosed early, can lead to massive resource loss and pose a serious threat to humans and animals who consume the infected apples. Our work proposes an ensembled system of Xception, InceptionResNet, and MobileNet architectures to detect 5 different types of apple plant diseases. The system has achieved outstanding results in multi-class and multi-label classification and can be used in a real-time setting to monitor large apple plantations.
  arXiv  Detail & Related papers  (2022-10-01T15:40:04Z)
• Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions [0.0]
  We create an open-source easy-to-use web application to address some of these issues which may help improve crop production. In particular, we support crop recommendation, fertilizer recommendation, plant disease prediction, and an interactive news-feed.
  arXiv  Detail & Related papers  (2022-04-24T19:31:10Z)
• 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 Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves [0.5485240256788552]
  This work focuses on two major diseases woodiness (viral) and brown spot (fungal) diseases. We have partnered with the Uganda National Crop Research Institute (NaCRRI) to develop a dataset of expertly labelled passion fruit plant leaves and fruits.
  arXiv  Detail & Related papers  (2020-07-28T10:17:43Z)
• Pollen13K: A Large Scale Microscope Pollen Grain Image Dataset [63.05335933454068]
  This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects. The paper focuses on the employed data acquisition steps, which include aerobiological sampling, microscope image acquisition, object detection, segmentation and labelling.
  arXiv  Detail & Related papers  (2020-07-09T10:33:31Z)
• 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.