Deep learning-based visual recognition of rumex for robotic precision farming

Kounalakis, Tsampikos; Triantafyllidis, Georgios; Nalpantidis, Lazaros

doi:10.1016/j.compag.2019.104973

Cited by 68 publications

(31 citation statements)

References 36 publications

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“…This kind of tradeoff between accuracy and computational cost could be addressed in technologies supporting AI in agriculture. So when there are some limitations and speed constraints, the more important metrics should be taken into account and compared to help to choose the right method [79]. However, there are studies that improve the accuracy of detection and speed of processing to make these suitable for real-time applications [50,52,103].…”

Section: Discussionmentioning

confidence: 99%

“…Bah et al [78] proposed a learning method using CNN for weed detection from images collected by UAV that automatically performed unsupervised training dataset collection. Kounalakis et al [79] combined classifier for weed recognition with transfer learning techniques for deep learning-based feature extraction. Partel et al [80] designed and developed a smart sprayer using machine vision and artificial intelligence.…”

Section: Weed Detectionmentioning

confidence: 99%

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Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

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Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

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Section: Discussionmentioning

confidence: 99%

Section: Weed Detectionmentioning

confidence: 99%

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

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“…Various methods in semi- and unsupervised fields have also emerged to reduce the labeling cost. In many cases, classification results obtained using these deep learning algorithms are better than those generated using traditional algorithms [ 123 ]. The use of traditional algorithms to classify different types of crops with high accuracy is still difficult.…”

Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

“…Corresponding mechanical knife devices were also designed for automatic control of weeds in tomato and lettuce fields, which could work efficiently in a high-weed density environment. The system proposed by Kounalakis et al [ 123 ] was mainly used to detect a specific plant on grassland, which would cause health, yield, and quality problems if eaten by animals. The implementation of this method relied on the design of a robot platform that could accurately detect the plant.…”

Section: Weeding Machinerymentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

171

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Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.

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“…In recent years, with the rapid development of computer, automation and artificial intelligence, robot technology has drawn tremendous attractions, since robots can be widely used for human-robot collaboration [1], robotic laser welding [2], robotic precision farming [3], and medical treatment [4]. As an important carrier of artificial intelligence, how to design intelligent robots has become an active research subject.…”

Section: Introductionmentioning

confidence: 99%

Designing a Supermarket Service Robot Based on Deep Convolutional Neural Networks

et al. 2020

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In order to save people’s shopping time and reduce labor cost of supermarket operations, this paper proposes to design a supermarket service robot based on deep convolutional neural networks (DCNNs). Firstly, according to the shopping environment and needs of supermarket, the hardware and software structure of supermarket service robot is designed. The robot uses a robot operating system (ROS) middleware on Raspberry PI as a control kernel to implement wireless communication with customers and staff. So as to move flexibly, the omnidirectional wheels symmetrically installed under the robot chassis are adopted for tracking. The robot uses an infrared detection module to detect whether there are commodities in the warehouse or shelves or not, thereby grasping and placing commodities accurately. Secondly, the recently-developed single shot multibox detector (SSD), as a typical DCNN model, is employed to detect and identify objects. Finally, in order to verify robot performance, a supermarket environment is designed to simulate real-world scenario for experiments. Experimental results show that the designed supermarket service robot can automatically complete the procurement and replenishment of commodities well and present promising performance on commodity detection and recognition tasks.

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Deep learning-based visual recognition of rumex for robotic precision farming

Cited by 68 publications

References 36 publications

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Review of Weed Detection Methods Based on Computer Vision

Designing a Supermarket Service Robot Based on Deep Convolutional Neural Networks

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