A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields

• URL: http://arxiv.org/abs/2405.04305v1
• Date: Tue, 7 May 2024 13:27:58 GMT
• Title: A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields
• Authors: Raiyan Rahman, Christopher Indris, Goetz Bramesfeld, Tianxiao Zhang, Kaidong Li, Xiangyu Chen, Ivan Grijalva, Brian McCornack, Daniel Flippo, Ajay Sharda, Guanghui Wang,
• Abstract summary: 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.
• Score: 9.735484847744416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address this problem, farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. As a result, a large amount of pesticide is wasted on areas without significant pest infestation. This brings to attention the urgent need for an intelligent autonomous system that can locate and spray sufficiently large infestations selectively within the complex crop canopies. We have developed a large multi-scale dataset for aphid cluster detection and segmentation, collected from actual sorghum fields and meticulously annotated to include clusters of aphids. Our dataset comprises a total of 54,742 image patches, showcasing a variety of viewpoints, diverse lighting conditions, and multiple scales, highlighting its effectiveness for real-world applications. In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection. Considering the balance between accuracy and efficiency, Fast-SCNN delivered the most effective segmentation results, achieving 80.46% mean precision, 81.21% mean recall, and 91.66 frames per second (FPS). For object detection, RT-DETR exhibited the best overall performance with a 61.63% mean average precision (mAP), 92.6% mean recall, and 72.55 on an NVIDIA V100 GPU. Our experiments further indicate that aphid cluster segmentation is more suitable for assessing aphid infestations than using detection models.

Related papers

• Multispectral Remote Sensing for Weed Detection in West Australian Agricultural Lands [3.6284577335311563]
  The Kondinin region in Western Australia faces significant agricultural challenges due to pervasive weed infestations, causing economic losses and ecological impacts. This study constructs a tailored multispectral remote sensing framework for weed detection to advance precision agriculture practices. Unmanned aerial vehicles were used to collect raw multispectral data from two experimental areas over four years, covering 0.6046 km2 and ground truth annotations were created with GPS-enabled vehicles to manually label weeds and crops.
  arXiv  Detail & Related papers  (2025-02-12T07:01:42Z)
• PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation [37.383095056084834]
  Plant disease datasets typically lack segmentation labels. Unlike typical datasets that contain images from laboratory settings, PlantSeg primarily comprises in-the-wild plant disease images. PlantSeg is extensive, featuring 11,400 images with disease segmentation masks and an additional 8,000 healthy plant images categorized by plant type.
  arXiv  Detail & Related papers  (2024-09-06T06:11:28Z)
• 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)
• On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild [13.402804225093801]
  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.
  arXiv  Detail & Related papers  (2023-07-17T19:04:39Z)
• 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)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• High performing ensemble of convolutional neural networks for insect pest image detection [124.23179560022761]
  Pest infestation is a major cause of crop damage and lost revenues worldwide. We generate ensembles of CNNs based on different topologies. Two new Adam algorithms for deep network optimization are proposed.
  arXiv  Detail & Related papers  (2021-08-28T00:49:11Z)
• An Efficient Insect Pest Classification Using Multiple Convolutional Neural Network Based Models [0.3222802562733786]
  Insect pest classification is a difficult task because of various kinds, scales, shapes, complex backgrounds in the field, and high appearance similarity among insect species. We present different convolutional neural network-based models in this work, including attention, feature pyramid, and fine-grained models. The experimental results show that combining these convolutional neural network-based models can better perform than the state-of-the-art methods on these two datasets.
  arXiv  Detail & Related papers  (2021-07-26T12:53:28Z)
• One-Shot Learning with Triplet Loss for Vegetation Classification Tasks [45.82374977939355]
  Triplet loss function is one of the options that can significantly improve the accuracy of the One-shot Learning tasks. Starting from 2015, many projects use Siamese networks and this kind of loss for face recognition and object classification.
  arXiv  Detail & Related papers  (2020-12-14T10:44:22Z)
• 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)

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.