On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild

• URL: http://arxiv.org/abs/2307.10267v1
• Date: Mon, 17 Jul 2023 19:04:39 GMT
• Title: On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild
• Authors: Raiyan Rahman, Christopher Indris, Tianxiao Zhang, Kaidong Li, Brian McCornack, Daniel Flippo, Ajay Sharda, Guanghui Wang
• Abstract summary: Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses. Farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. We propose the use of real-time semantic segmentation models to segment clusters of aphids.
• Score: 13.402804225093801
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. As a result, a considerable amount of pesticide is wasted on areas without pests, while inadequate amounts are applied to areas with severe infestations. The paper focuses on the urgent need for an intelligent autonomous system that can locate and spray infestations within complex crop canopies, reducing pesticide use and environmental impact. We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids. A multiscale dataset is generated to allow for learning the clusters at different scales. We compare the segmentation speeds and accuracy of four state-of-the-art real-time semantic segmentation models on the aphid cluster dataset, benchmarking them against nonreal-time models. The study results show the effectiveness of a real-time solution, which can reduce inefficient pesticide use and increase crop yields, paving the way towards an autonomous pest detection system.

Related papers

• A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields [9.735484847744416]
  Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields. Farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. We have developed a large multi-scale dataset for aphid cluster detection and segmentation.
  arXiv  Detail & Related papers  (2024-05-07T13:27:58Z)
• 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)
• Aphid Cluster Recognition and Detection in the Wild Using Deep Learning Models [17.65292847038642]
  Aphid infestation poses a significant threat to crop production, rural communities, and global food security. This paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters.
  arXiv  Detail & Related papers  (2023-08-10T23:53:07Z)
• A New Dataset and Comparative Study for Aphid Cluster Detection [17.65292847038642]
  Aphids are one of the main threats to crops, rural families, and global food security. accurately localizing the aphid infestation and estimating the level is crucial to the precise local application of pesticides. We have taken millions of images in the sorghum fields, manually selected 5,447 images that contain aphids, and annotated each aphid cluster in the image.
  arXiv  Detail & Related papers  (2023-07-12T05:49:21Z)
• 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)
• Spatial Monitoring and Insect Behavioural Analysis Using Computer Vision for Precision Pollination [6.2997667081978825]
  Insects are the most important global pollinator of crops and play a key role in maintaining the sustainability of natural ecosystems. Current computer vision facilitated insect tracking in complex outdoor environments is restricted in spatial coverage. This article introduces a novel system to facilitate markerless data capture for insect counting, insect motion tracking, behaviour analysis and pollination prediction.
  arXiv  Detail & Related papers  (2022-05-10T05:11:28Z)
• 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)
• ZeroWaste Dataset: Towards Automated Waste Recycling [51.053682077915546]
  We present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. This dataset contains over1800fully segmented video frames collected from a real waste sorting plant. We show that state-of-the-art segmentation methods struggle to correctly detect and classify target objects.
  arXiv  Detail & Related papers  (2021-06-04T22:17:09Z)
• 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)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)
• Weakly Supervised Learning Guided by Activation Mapping Applied to a Novel Citrus Pest Benchmark [6.239768930024569]
  Integrated Pest Management is the most widely used process to prevent and mitigate the damages caused by pests and diseases in citrus crops. We design a weakly supervised learning process guided by saliency maps to automatically select regions of interest in the images. Experiments conducted on two large datasets demonstrate that our results are very promising for the problem of pest and disease classification in the agriculture field.
  arXiv  Detail & Related papers  (2020-04-22T01:26:50Z)

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.