Design and Implementation of a Weed Removal Agriculture Robot

Dhanasekar, J.; Kumar, B. Sathish; Akash, S.; Balamurugan, P.; Vasanth, G.; Umamaheswari, B.

doi:10.1007/978-981-19-1742-4_46

Cited by 1 publication

(1 citation statement)

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“…Technology helps reduce the use of fertilizers, pesticides (Mogili and Deepak, 2018), and water wastage (Pawlowski et al, 2017). Agricultural activities significantly benefit from technology, including applying micronutrients (Lopez-Castro et al, 2020), estimating the quality and quantity of fruits (Gongal et al, 2018), and the mechanical or electromagnetic removal of weeds (Dhanasekar et al, 2022). Robots can be helpful as autonomous platforms capable of performing the mentioned tasks and, in addition, can be used to acquire a continuously large amount of crop data.…”

Section: Introductionmentioning

confidence: 99%

MAgro dataset: A dataset for simultaneous localization and mapping in agricultural environments

Marzoa Tanco,

Trinidad Barnech,

Andrade

et al. 2023

The International Journal of Robotics Research

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The agricultural industry is being transformed, thanks to recent innovations in computer vision and deep learning. However, the lack of specific datasets collected in natural agricultural environments is, arguably, the main bottleneck for novel discoveries and benchmarking. The present work provides a novel dataset, Magro, and a framework to expand data collection. We present the first version of the Magro Dataset V1.0, consisting of nine ROS bags (and the corresponding raw data) containing data collected in apple and pear crops. Data were gathered, repeating a fixed trajectory on different days under different illumination and weather conditions. To support the evaluation of loop closure algorithms, the trajectories are designed to have loop closures, revisiting some places from different viewpoints. We use a Clearpath’s Jackal robot equipped with stereo cameras pointing to the front and left side, a 3D LIDAR, three inertial measurement units (IMU), and wheel encoders. Additionally, we provide calibrated RTK GPS data that can be used as ground truth. Our dataset is openly available, and it will be updated to have more data and variability. Finally, we tested two existing state-of-the-art algorithms for vision and point cloud-based localization and mapping on our novel dataset to validate the dataset’s usability.

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