A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants

• URL: http://arxiv.org/abs/2004.04023v1
• Date: Wed, 8 Apr 2020 14:41:38 GMT
• Title: A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants
• Authors: Denis P.Rubanga, Loyani K. Loyani, Mgaya Richard, Sawahiko Shimada
• Abstract summary: We propose a Convolutional Neural Network (CNN) approach in determining the effects of Tuta absoluta in tomato plants. Four CNN pre-trained architectures were used in training classifiers on a dataset containing health and infested tomato leaves. Inception-V3 yielded the best results with an average accuracy of 87.2 percent in estimating the severity status of Tuta absoluta in tomato plants.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early quantification of Tuta absoluta pest's effects in tomato plants is a very important factor in controlling and preventing serious damages of the pest. The invasion of Tuta absoluta is considered a major threat to tomato production causing heavy loss ranging from 80 to 100 percent when not properly managed. Therefore, real-time and early quantification of tomato leaf miner Tuta absoluta, can play an important role in addressing the issue of pest management and enhance farmers' decisions. In this study, we propose a Convolutional Neural Network (CNN) approach in determining the effects of Tuta absoluta in tomato plants. Four CNN pre-trained architectures (VGG16, VGG19, ResNet and Inception-V3) were used in training classifiers on a dataset containing health and infested tomato leaves collected from real field experiments. Among the pre-trained architectures, experimental results showed that Inception-V3 yielded the best results with an average accuracy of 87.2 percent in estimating the severity status of Tuta absoluta in tomato plants. The pre-trained models could also easily identify High Tuta severity status compared to other severity status (Low tuta and No tuta)

Related papers

• Drone-Based Multispectral Imaging and Deep Learning for Timely Detection of Branched Broomrape in Tomato Farms [0.2770822269241974]
  This study addresses the escalating threat of branched broomrape (Phelipanche ramosa) to California's tomato industry.<n>The parasite's largely underground life cycle makes early detection difficult, while conventional chemical controls are costly, environmentally harmful, and often ineffective.<n>We combined drone-based multispectral imagery with Long Short-Term Memory (LSTM) deep learning networks, using the Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance.
  arXiv  Detail & Related papers  (2025-09-12T05:16:56Z)
• Fact or Facsimile? Evaluating the Factual Robustness of Modern Retrievers [34.31192184496381]
  Dense retrievers and rerankers are central to retrieval-augmented generation (RAG) pipelines.<n>We evaluate how much factual competence these components inherit or lose from large language models (LLMs) they are based on.<n>For every embedding model, cosine-similarity scores between queries and correct completions are significantly higher than those for incorrect ones.
  arXiv  Detail & Related papers  (2025-08-28T04:13:51Z)
• Development of an Improved Capsule-Yolo Network for Automatic Tomato Plant Disease Early Detection and Diagnosis [0.0]
  tomato diseases can often be visually identified through distinct forms, appearances, or textures.<n>This study presents an enhanced Capsule-YOLO network architecture designed to automatically segment overlapping and occluded tomato leaf images.
  arXiv  Detail & Related papers  (2025-07-03T23:24:12Z)
• Optimized Custom CNN for Real-Time Tomato Leaf Disease Detection [0.0]
  In Bangladesh, tomatoes are a staple vegetable, prized for their versatility in various culinary applications. Early detection of diseases is crucial for implementing timely interventions and ensuring the sustainability of tomato production. Traditional manual inspection methods, while effective, are labor-intensive and prone to human error. This research paper sought to develop an automated disease detection system using Convolutional Neural Networks (CNNs)
  arXiv  Detail & Related papers  (2025-02-23T18:27:08Z)
• Towards Efficient and Intelligent Laser Weeding: Method and Dataset for Weed Stem Detection [51.65457287518379]
  This study is the first empirical investigation of weed recognition for laser weeding. We integrate the detection of crop and weed with the localization of weed stem into one end-to-end system. The proposed system improves weeding accuracy by 6.7% and reduces energy cost by 32.3% compared to existing weed recognition systems.
  arXiv  Detail & Related papers  (2025-02-10T08:42:46Z)
• Aggrotech: Leveraging Deep Learning for Sustainable Tomato Disease Management [0.0]
  We propose a deep learning-based approach for Tomato Leaf Disease Detection using two well-established convolutional neural networks (CNNs) The research employs VGG19 and Inception v3 models on the Tomato Villages dataset (4525 images) for tomato leaf disease detection. The models' accuracy of 93.93% with dropout layers demonstrates their usefulness for crop health monitoring.
  arXiv  Detail & Related papers  (2025-01-21T11:25:44Z)
• Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD [3.285994579445155]
  This paper introduces a cutting-edge technique for the detection and classification of tomato leaf diseases. We propose a sophisticated approach within the domain of subspace learning, known as Higher-Order Whitened Singular Value Decomposition. The efficacy of this innovative method was rigorously tested through comprehensive experiments on two distinct datasets.
  arXiv  Detail & Related papers  (2024-05-30T13:46:56Z)
• Smoke and Mirrors in Causal Downstream Tasks [59.90654397037007]
  This paper looks at the causal inference task of treatment effect estimation, where the outcome of interest is recorded in high-dimensional observations. We compare 6 480 models fine-tuned from state-of-the-art visual backbones, and find that the sampling and modeling choices significantly affect the accuracy of the causal estimate. Our results suggest that future benchmarks should carefully consider real downstream scientific questions, especially causal ones.
  arXiv  Detail & Related papers  (2024-05-27T13:26:34Z)
• Machine Learning in management of precautionary closures caused by lipophilic biotoxins [43.51581973358462]
  Mussel farming is one of the most important aquaculture industries. The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption. This work proposes a predictive model capable of supporting the application of precautionary closures.
  arXiv  Detail & Related papers  (2024-02-14T15:51:58Z)
• Early and Accurate Detection of Tomato Leaf Diseases Using TomFormer [0.3169023552218211]
  This paper introduces a transformer-based model called TomFormer for the purpose of tomato leaf disease detection. We present a novel approach for detecting tomato leaf diseases by employing a fusion model that combines a visual transformer and a convolutional neural network.
  arXiv  Detail & Related papers  (2023-12-26T20:47:23Z)
• DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization [43.60484692738197]
  Visual reinforcement learning has shown promise in continuous control tasks. Current algorithms are still unsatisfactory in virtually every aspect of the performance. DrM is the first model-free algorithm that consistently solves tasks in both the Dog and Manipulator domains.
  arXiv  Detail & Related papers  (2023-10-30T15:50:56Z)
• Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches [0.6226366855893847]
  Early identification of abnormalities in plants is an important task for ensuring proper growth and achieving high yields from crops. We have developed a methodology which can detect different levels of abnormality in maize plants independently of their growth stage. Preliminary results show an 88.89% detection accuracy of low abnormality and 100% detection accuracy of no abnormality.
  arXiv  Detail & Related papers  (2023-10-20T00:06:42Z)
• Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias [64.81358555107788]
  Pre-trained Language Models (PLMs) may be poisonous with backdoors or bias injected by the suspicious attacker during the fine-tuning process. We propose the Fine-purifying approach, which utilizes the diffusion theory to study the dynamic process of fine-tuning for finding potentially poisonous dimensions. To the best of our knowledge, we are the first to study the dynamics guided by the diffusion theory for safety or defense purposes.
  arXiv  Detail & Related papers  (2023-05-08T08:40:30Z)
• Detection of Tomato Ripening Stages using Yolov3-tiny [0.0]
  We use a neural network-based model for tomato classification and detection. Our experiments showed an f1-score of 90.0% in the localization and classification of ripening stages in a custom dataset.
  arXiv  Detail & Related papers  (2023-02-01T00:57:58Z)
• Siamese Network-based Lightweight Framework for Tomato Leaf Disease Recognition [0.0]
  A novel Siamese network-based lightweight framework is proposed for automatic tomato leaf disease recognition. It achieves the highest accuracy of 96.97% on the tomato subset and 95.48% on the Taiwan tomato leaf disease dataset.
  arXiv  Detail & Related papers  (2022-09-18T16:08:07Z)
• 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)
• Geometry Uncertainty Projection Network for Monocular 3D Object Detection [138.24798140338095]
  We propose a Geometry Uncertainty Projection Network (GUP Net) to tackle the error amplification problem at both inference and training stages. Specifically, a GUP module is proposed to obtains the geometry-guided uncertainty of the inferred depth. At the training stage, we propose a Hierarchical Task Learning strategy to reduce the instability caused by error amplification.
  arXiv  Detail & Related papers  (2021-07-29T06:59:07Z)
• A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery [56.10033255997329]
  We propose a novel deep learning method based on a Convolutional Neural Network (CNN) It simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops.
  arXiv  Detail & Related papers  (2020-12-31T18:51:17Z)
• 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)

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.