Farm Workers of the Future: Vision-Based Robotics for Broad-Acre Agriculture

Ball, David; Ross, Patrick; English, Andrew; Milani, Peter; Richards, Daniel; Bate, Andrew; Upcroft, Ben; Wyeth, Gordon; Corke, Peter

doi:10.1109/mra.2016.2616541

Cited by 35 publications

(16 citation statements)

References 23 publications

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“…We performed data augmentation on the training samples by random rotated [-5, 5] KITTI dataset [9] is made up of several outdoor scenes captured by LIDAR sensor and car-mounted cameras at resolution of roughly 375×1242. The train and test splits we employ followed the method in [6]. We used about 22k images for training, and 697 test images from different scenes.…”

Section: ) Datasetsmentioning

confidence: 99%

“…Monocular depth estimation is a fundamental but challenging task in computer vision, the goal of which is to predict a dense depth map from a given image. The technical progress in this area can be applied to widespread applications, such as scene understanding [1], [2], action recognition [3], 3D reconstruction [4], robotics [5], [6], etc. However, it is still a very challenging topic since one image may correspond to several real scenes and there are no other available clues, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Joint Attention Mechanisms for Monocular Depth Estimation With Multi-Scale Convolutions and Adaptive Weight Adjustment

et al. 2020

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Monocular depth estimation is a fundamental problem for various vision applications, and is therefore gaining increasing attention in the field of computer vision. Though a great improvement has been made thanks to the rapid progress of deep convolutional neural networks, depth estimation of the object at finer details remains an unsatisfactory issue, especially in complex scenes that has rich structure information. In this paper, we proposed a deep end-to-end learning framework with the combination of multi-scale convolutions and joint attention mechanisms to tackle this challenge. Specifically, we firstly elaborately designed a lightweight up-convolution to generate multi-scale feature maps. Then we introduced an attention-based residual block to aggregate different feature maps in joint channel and spatial dimension, which could enhance the discriminant ability of feature fusion at finer details. Furthermore, we explored an effective adaptive weight adjustment strategy for the loss function to further improve the performance, which adjusts the weight of each loss term during training without additional hyperparameters. The proposed framework was evaluated using challenging NYU Depth v2 and KITTI datasets. Experimental results demonstrated that the proposed approach is superior to most of the state-of-the-art methods. INDEX TERMS Monocular depth estimation, multi-scale convolutions, joint attention mechanisms, weight adjustment.

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Section: ) Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Attention Mechanisms for Monocular Depth Estimation With Multi-Scale Convolutions and Adaptive Weight Adjustment

et al. 2020

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“…The agricultural industry will be more economically sustainable by automating subminimum-wage jobs. Examples of technological advancements in agriculture yet to be widely adopted include harvesting robots, driverless tractors, sprayer drones, artificial intelligence to manage chemical and fertilizer application, and precision dairy farming, among others (Ball et al 2017, Smith 2017.…”

Section: Future Researchmentioning

confidence: 99%

“…Workers will have the opportunity to move up in the value chain with new job tasks such as managing fleets of robots rather than spraying chemicals or cleaning cow udders. Other types of jobs will be, for example, to analyze and interpret data coming from artificial intelligence, allowing better decisions based on more detailed and higherquality information (Ball et al 2017, Smith, 2017 and a consideration of wider societal benefits such as environmental impacts and animal health.…”

Section: Future Researchmentioning

confidence: 99%

Adoption of Labor-Saving Technologies in Agriculture

Gallardo

Sauer

2018

Annu. Rev. Resour. Econ.

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Labor-saving technologies in agriculture have been fundamental to the advancement of the agricultural industry, and in general, the economies of nations. This article presents a review of several economic theories that form the basis of the economics of labor-saving technologies, including the theory of induced innovation and subsequent theories developed from it. The review also includes empirical application studies and classifies existing literature into ex ante and ex post analyses of technology adoption. It also presents a thorough review of economic studies on the most successful labor-saving technology adoptions in agriculture, including crops and livestock. Finally, we discuss the future of labor-saving technologies in agriculture and their implications for new societal and economic structures.

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“…Also, there are automated irrigation and fire alert system based on Hargreaves equation using weather forecast and ZigBee Protocol along with GSM protocol [10]. The challenge of energy availability and the price is still one of the greatest challenges in farm areas, so design a WSN using low cost and low energy efficient framework, using Eco-friendly and energy efficient Sensor technology for precision agriculture irrigation is required to achieve sustainability ( [11], [12]). …”

Section: Literature Reviewmentioning

confidence: 99%

Precision Agriculture Monitoring System using Internet of Things (IoT)

Ali¹

2018

IJRASET

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There are many agriculture applications incorporating IoT to increase the productivity of the crops, however, these applications are still facing many challenges. The researchers are working to defeat these challenges in the futuristic applications by deploying new technologies; these technologies are not easy to be used by farmers. So, this research work aims to build a real-time, cost-effective Precision Agriculture Monitoring System with a user-friendly interface to help the farmers monitor their farm parameter's variation (weather, water, soil, pest detection, intrusion detection, fire detection) periodically from anywhere and at any time using their smart-phones. The proposed system will act as a decision support system helps farmers to take the appropriate actions based on the farm parameter's variation; by sending an alert email to the farmer when it is required.

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Farm Workers of the Future: Vision-Based Robotics for Broad-Acre Agriculture

Cited by 35 publications

References 23 publications

Joint Attention Mechanisms for Monocular Depth Estimation With Multi-Scale Convolutions and Adaptive Weight Adjustment

Joint Attention Mechanisms for Monocular Depth Estimation With Multi-Scale Convolutions and Adaptive Weight Adjustment

Adoption of Labor-Saving Technologies in Agriculture

Precision Agriculture Monitoring System using Internet of Things (IoT)

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