Visual Trunk Detection Using Transfer Learning and a Deep Learning-Based Coprocessor

Aguiar, André Silva Pinto de; Santos, Filipe Neves dos; Sousa, Armando; Oliveira, P. B. de Moura; Santos, Leonardo D.

doi:10.1109/access.2020.2989052

Cited by 36 publications

(19 citation statements)

References 38 publications

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“…DNNs are typically operate directly on raw images and actively learn a variety of filter parameters during the training of a model (Pound et al, 2017;Irshat et al, 2018). Aguiar et al (2020) presented a DNN models to detect the vine trunks as reliable features and landmarks to navigate a mobile robot in a vineyard. Parhar et al (2018) used variation of Generative Adversarial Network (GAN) to detect the stalk of sorghum in the field and grasp it by a robotic manipulator.…”

mentioning

confidence: 99%

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Atefi

Pitla

et al. 2021

Front. Plant Sci.

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Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era.

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mentioning

confidence: 99%

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Atefi

Pitla

et al. 2021

Front. Plant Sci.

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“…Then, the mapping approaches are divided in two main sets: metric maps, topological maps, and semantic maps. [38]. The image shows the complexity of the mapping procedure, and the importance of the projection of an adequate system of sensors.…”

Section: Mapping the Environmentmentioning

confidence: 99%

“…Figure 3. Agricultural robot working place on an agricultural environment[38]. The image shows the complexity of the mapping procedure, and the importance of the projection of an adequate system of sensors.…”

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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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“…Our previous works focused on the detection of vine trunks [14][15][16] and tomatoes in greenhouses [17]. This work intends to solve the problem of automatically detecting grape bunches in images considering different growth stages, so that more intelligent and advanced tasks can be performed by robots such as: harvesting, yield estimation, fruit picking, semantic mapping of cultures, and others.…”

Section: Introductionmentioning

confidence: 99%

Grape Bunch Detection at Different Growth Stages Using Deep Learning Quantized Models

et al. 2021

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The agricultural sector plays a fundamental role in our society, where it is increasingly important to automate processes, which can generate beneficial impacts in the productivity and quality of products. Perception and computer vision approaches can be fundamental in the implementation of robotics in agriculture. In particular, deep learning can be used for image classification or object detection, endowing machines with the capability to perform operations in the agriculture context. In this work, deep learning was used for the detection of grape bunches in vineyards considering different growth stages : the early stage just after the bloom and the medium stage where the grape bunches present an intermediate development. Two state-of-the-art single-shot multibox models were trained, quantized, and deployed in a low-cost and low-power hardware device, a Tensor Processing Unit. The training input was a novel and publicly available dataset proposed in this work. This dataset contains 1929 images and respective annotations of grape bunches at two different growth stages, captured by different cameras in several illumination conditions. The models were benchmarked and characterized considering the variation of two different parameters: the confidence score and the intersection over union threshold. The results showed that the deployed models could detect grape bunches in images with a medium average precision up to 66.96%. Since this approach uses low resources, a low-cost and low-power hardware device that requires simplified models with 8 bit quantization, the obtained performance was satisfactory. Experiments also demonstrated that the models performed better in identifying grape bunches at the medium growth stage, in comparison with grape bunches present in the vineyard after the bloom, since the second class represents smaller grape bunches, with a color and texture more similar to the surrounding foliage, which complicates their detection.

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Visual Trunk Detection Using Transfer Learning and a Deep Learning-Based Coprocessor

Cited by 36 publications

References 38 publications

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

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

Grape Bunch Detection at Different Growth Stages Using Deep Learning Quantized Models

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