Vineyard trunk detection using deep learning – An experimental device benchmark

Aguiar, André Silva Pinto de; Santos, Filipe; Santos, Leonardo D.; Filipe, Vítor; Sousa, Armando

doi:10.1016/j.compag.2020.105535

Cited by 38 publications

(32 citation statements)

References 32 publications

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“…Detection frameworks are devised to automate pig monitoring, a use case where implementing solutions with low cost is tremendously important given the potential need for large-scale deployment of such systems and the more than likely high turnover rate due to their rapid physical deterioration. Nevertheless, except for Seo et al's work, the different observed approaches are all related to activities of the agricultural sector, a context where detection techniques stand out as an effective method to recognize plant diseases [80,107], as well as an essential service integrated into the control software of agricultural robots [76,87,91,103]. Specifically regarding the last point, leveraging mobile agricultural robots makes it possible to automate a considerable part of traditional farmers' tasks, eminently repetitive, such as fruit counting [87,103], harvesting, and picking [76].…”

Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

“…Specifically regarding the last point, leveraging mobile agricultural robots makes it possible to automate a considerable part of traditional farmers' tasks, eminently repetitive, such as fruit counting [87,103], harvesting, and picking [76]. Fruit detection, as well as the detection of any other typical element in such contexts like trunks or branches, is exploited not only for the indicated purpose but also to provide the robot with information on the environment necessary to successfully navigate through the crop fields [91], often irregular or located in areas where the GPS signal is not reliable enough.…”

Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

“…The result is a simplified and smaller architecture, conceived as a single feedforward neural network, suitable to derive high-inference-speed detection models. The unified approach, crystallized in compact architectures considered a standard nowadays, such as SSD [118] and YOLO in its different versions [119][120][121][122], constitute the foundation on which modern lightweight object detection architectures have been built in AmI [48,49,52,54,55,63,65,66,68,70,72,73,75,[77][78][79][80][81][82][86][87][88]90,91,94,100,[105][106][107]. Such compact architectures, however, resulted from an eminently moderate structural optimization in a first attempt to make CNNs more manageable, prioritizing accuracy preservation over structural reduction and inference speed increase.…”

Section: Architecturesmentioning

confidence: 99%

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On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

2021

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Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing data from environments and extracting high-level knowledge to detect or recognize users’ features and actions, as well as entities or events in their surroundings. Visual perception, particularly object detection, has become one of the most relevant enabling factors for this context-aware user-centered intelligence, being the cornerstone of relevant but complex tasks, such as object tracking or human action recognition. In this context, convolutional neural networks have proven to achieve state-of-the-art accuracy levels. However, they typically result in large and highly complex models that typically demand computation offloading onto remote cloud platforms. Such an approach has security- and latency-related limitations and may not be appropriate for some AmI use cases where the system response time must be as short as possible, and data privacy must be guaranteed. In the last few years, the on-device paradigm has emerged in response to those limitations, yielding more compact and efficient neural networks able to address inference directly on client machines, thus providing users with a smoother and better-tailored experience, with no need of sharing their data with an outsourced service. Framed in that novel paradigm, this work presents a review of the recent advances made along those lines in object detection, providing a comprehensive study of the most relevant lightweight CNN-based detection frameworks, discussing the most paradigmatic AmI domains where such an approach has been successfully applied, the different challenges arisen, the key strategies and techniques adopted to create visual solutions for image-based object classification and localization, as well as the most relevant factors to bear in mind when assessing or comparing those techniques, such as the evaluation metrics or the hardware setups used.

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Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

Section: Architecturesmentioning

confidence: 99%

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On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

2021

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“…Our previous works [43,44] focused on the usage and benchmark of low-power devices to deploy DL models while using a low quantity of training data. In this paper, the semantic vineyard perception problem is extended with the following main contributions and innovations:…”

Section: Introductionmentioning

confidence: 99%

Bringing Semantics to the Vineyard: An Approach on Deep Learning-Based Vine Trunk Detection

et al. 2021

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The development of robotic solutions in unstructured environments brings several challenges, mainly in developing safe and reliable navigation solutions. Agricultural environments are particularly unstructured and, therefore, challenging to the implementation of robotics. An example of this is the mountain vineyards, built-in steep slope hills, which are characterized by satellite signal blockage, terrain irregularities, harsh ground inclinations, and others. All of these factors impose the implementation of precise and reliable navigation algorithms, so that robots can operate safely. This work proposes the detection of semantic natural landmarks that are to be used in Simultaneous Localization and Mapping algorithms. Thus, Deep Learning models were trained and deployed to detect vine trunks. As significant contributions, we made available a novel vine trunk dataset, called VineSet, which was constituted by more than 9000 images and respective annotations for each trunk. VineSet was used to train state-of-the-art Single Shot Multibox Detector models. Additionally, we deployed these models in an Edge-AI fashion and achieve high frame rate execution. Finally, an assisted annotation tool was proposed to make the process of dataset building easier and improve models incrementally. The experiments show that our trained models can detect trunks with an Average Precision up to 84.16% and our assisted annotation tool facilitates the annotation process, even in other areas of agriculture, such as orchards and forests. Additional experiments were performed, where the impact of the amount of training data and the comparison between using Transfer Learning and training from scratch were evaluated. In these cases, some theoretical assumptions were verified.

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“…The most common solution is to use Global Navigation Satellite System (GNSS) standalone-based solutions [5,6]. However, in many agricultural and forestry places, satellite signals suffer from signal blockage and multi-reflection [7,8], making the use of GNSS unreliable. In this context, it is extremely important to research and develop intelligent solutions that use different modalities of sensors, as well as different sources of input 2.1.…”

Section: Introductionmentioning

confidence: 99%

Localization and Mapping for Robots in Agriculture and Forestry: A Survey

et al. 2020

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Research and development of autonomous mobile robotic solutions that can perform several active agricultural tasks (pruning, harvesting, mowing) have been growing. Robots are now used for a variety of tasks such as planting, harvesting, environmental monitoring, supply of water and nutrients, and others. To do so, robots need to be able to perform online localization and, if desired, mapping. The most used approach for localization in agricultural applications is based in standalone Global Navigation Satellite System-based systems. However, in many agricultural and forest environments, satellite signals are unavailable or inaccurate, which leads to the need of advanced solutions independent from these signals. Approaches like simultaneous localization and mapping and visual odometry are the most promising solutions to increase localization reliability and availability. This work leads to the main conclusion that, few methods can achieve simultaneously the desired goals of scalability, availability, and accuracy, due to the challenges imposed by these harsh environments. In the near future, novel contributions to this field are expected that will help one to achieve the desired goals, with the development of more advanced techniques, based on 3D localization, and semantic and topological mapping. In this context, this work proposes an analysis of the current state-of-the-art of localization and mapping approaches in agriculture and forest environments. Additionally, an overview about the available datasets to develop and test these approaches is performed. Finally, a critical analysis of this research field is done, with the characterization of the literature using a variety of metrics.

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Vineyard trunk detection using deep learning – An experimental device benchmark

Cited by 38 publications

References 32 publications

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

Bringing Semantics to the Vineyard: An Approach on Deep Learning-Based Vine Trunk Detection

Localization and Mapping for Robots in Agriculture and Forestry: A Survey

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