A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases

Gutierrez, Aitor; Ansuategi, Ander; Susperregi, Loreto; Tubío, Carlos; Rankić, Ivan; Lenža, Libor

doi:10.1155/2019/5219471

Cited by 79 publications

(37 citation statements)

References 18 publications

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“…Furthermore, Gutierrez et al [42] conducted a study for monitoring and identifying whiteflies. For this purpose, two cameras, a dataset generator and two microcontrollers were used in combination with a K-nearest neighbor (KNN) and multilayer perceptron (MLP), resulting in an accuracy of between 66% and 81%.…”

Section: Automatic Monitoring Of Sucking Insectsmentioning

confidence: 99%

“…One of the biggest challenges of horticultural and fruit production in the Mediterranean, tropical, and subtropical areas of the world is the frugivorous fruit flies (Diptera: Tephritidae) [42]. This group of pest causes crop losses amounting to billions of dollars each year worldwide, totaling USD 242 million/year in Brazil alone [43].…”

Section: Automatic Identification and Monitoring Of Fruit Fliesmentioning

confidence: 99%

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Automatic Detection and Monitoring of Insect Pests—A Review

et al. 2020

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Many species of insect pests can be detected and monitored automatically. Several systems have been designed in order to improve integrated pest management (IPM) in the context of precision agriculture. Automatic detection traps have been developed for many important pests. These techniques and new technologies are very promising for the early detection and monitoring of aggressive and quarantine pests. The aim of the present paper is to review the techniques and scientific state of the art of the use of sensors for automatic detection and monitoring of insect pests. The paper focuses on the methods for identification of pests based in infrared sensors, audio sensors and image-based classification, presenting the different systems available, examples of applications and recent developments, including machine learning and Internet of Things. Future trends of automatic traps and decision support systems are also discussed.

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Section: Automatic Monitoring Of Sucking Insectsmentioning

confidence: 99%

Section: Automatic Identification and Monitoring Of Fruit Fliesmentioning

confidence: 99%

Automatic Detection and Monitoring of Insect Pests—A Review

et al. 2020

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“…Recent developments in Convolutional Neural Networks (ConvNets) have led to substantial progress in the performance of computer vision tasks applied across various domains such as self-driving cars [ 1 ], medical imaging [ 2 ], agriculture [ 3 , 4 ], manufacturing [ 5 ], etc. The availability of big data [ 6 ], together with increased computing capabilities is the predominant reason for the recent success.…”

Section: Introductionmentioning

confidence: 99%

A survey on generative adversarial networks for imbalance problems in computer vision tasks

et al. 2021

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Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

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“…The experimental results indicate that the model took 0.083 s inference time and scored mAP of 0.8922. Gutierrez et al [ 32 ] evaluated the efficiency of computer vision, machine learning, and deep learning algorithms for pest detection in tomato farms. The evaluation study indicates that the deep learning framework provided a comparatively better solution among the three options.…”

Section: Introductionmentioning

confidence: 99%

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

Ramalingam

Mohan

Pookkuttath

et al. 2020

Sensors

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Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.

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A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases

Cited by 79 publications

References 18 publications

Automatic Detection and Monitoring of Insect Pests—A Review

Automatic Detection and Monitoring of Insect Pests—A Review

A survey on generative adversarial networks for imbalance problems in computer vision tasks

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

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