Design and Development of Intelligent Pesticide Spraying System for Agricultural Robot

Deshmukh, Deepak; Pratihar, Dilip Kumar; Deb, Alok Kanti; Ray, Hena; Bhattacharyya, Nabarun

doi:10.1007/978-3-030-73050-5_16

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…Finally, in order to maintain real-time performance even without hardware accelerators, we employ a 224x224 low-resolution input. So, we rescale the global average pooling layer setting the kernel size to 12×12 with strides (4,5). Additionally, to have equal input and output dimensions, we add a final bilinear upsampling with a factor of 8 at the end of the segmentation head.…”

Section: Segmentation Networkmentioning

confidence: 99%

“…Agriculture 3.0 and 4.0 have gradually introduced autonomous machines and interconnected sensors into several agricultural processes [1], trying to introduce robust and costeffective novel solutions into the overall production chain. For instance, precision agriculture has progressively innovated tools for automatic harvesting [2], vegetative assessment [3], crops yield estimation [4], smart and sustainable pesticide spraying robotic system [5] and many others [6], [7]. Indeed, the pervasiveness of precision agriculture techniques has such a huge impact on certain fields of application that the adoption of them has become increasingly essential to achieve high product quality standards [8].…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

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A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops

Cerrato¹,

Mazzia²,

Salvetti³

et al. 2021

Preprint

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Expensive sensors and inefficient algorithmic pipelines significantly affect the overall cost of autonomous machines. However, affordable robotic solutions are essential to practical usage, and their financial impact constitutes a fundamental requirement to employ service robotics in most fields of application. Among all, researchers in the precision agriculture domain strive to devise robust and cost-effective autonomous platforms in order to provide genuinely large-scale competitive solutions. In this article, we present a complete algorithmic pipeline for row-based crops autonomous navigation, specifically designed to cope with low-range sensors and seasonal variations. Firstly, we build on a robust data-driven methodology to generate a viable path for the autonomous machine, covering the full extension of the crop with only the occupancy grid map information of the field. Moreover, our solution leverages on latest advancement of deep learning optimization techniques and synthetic generation of data to provide an affordable solution that efficiently tackles the well-known Global Navigation Satellite System unreliability and degradation due to vegetation growing inside rows. Extensive experimentation and simulations against computer-generated environments and real-world crops demonstrated the robustness and intrinsic generalizability of our methodology that opens the possibility of highly affordable and fully autonomous machines.

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Section: Segmentation Networkmentioning

confidence: 99%

Section: Introduction and State Of The Artmentioning

confidence: 99%

A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops

Cerrato¹,

Mazzia²,

Salvetti³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Using artificial intelligence in the wheeled mobile robot made it smart for following paths in pesticide spraying with minimum error [16][17][18][19][20]. Also, some other types of robots are employed to spray chemicals on the tree or farmyard.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Pesticide Sprayer Robot with a Color-based Vision System

Tahmasebi

Gohari

Emami

2022

IJRCS

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The usage of robots has been due to cost reduction and increasing operation accuracy. Furthermore, employing robots in farming can decrease human tasks in hard and dangerous duties such as plowing, spraying pesticides, etc. Spraying chemicals is a common operation in agriculture crop protection. This operation is essential, but it can create some problems such as human and environmental damages by overdosing using pesticides. Recently, researchers focused on precision agriculture to make this smart. Sensors are employed to detect leaves of plants on the ground and spray them as much as required. Thus, pesticide dose will be under control. The current paper aims to introduce a wheeled robot that is developed to detect plants by color sensor and spray them. This robot can move between planting rows and detect weeds based on the leave color. A microcontroller-based board was used as the main controller, which sends spray commands to the sprayer nozzle. Outdoor and indoor tests were carried out to study the accuracy of this system. Results of experiments showed that this robot could work with acceptable accuracy in identifying weeds in the field. Thus, this robot can be commercialized for applying in the field to spray pesticides.

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“…Besides image processing systems, self-driving machines represent a crucial component to reduce the costs of agricultural processes by providing autonomous, full-time and weather-independent operators. In this regard, the confidence in solutions based on autonomous Unmanned Ground Vehicles (UGVs) and UAVs is drastically increasing [30,31], considering the competitive advantage they can provide supporting tasks such as crop monitoring [35], spraying [8], grasping [14], and harvesting [19]. In this context, designing a reliable autonomous navigation system in constrained row-based crops such as vineyards and orchards is fundamental.…”

Section: Introductionmentioning

confidence: 99%

Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering

Salvetti¹,

Angarano²,

Martini³

et al. 2022

Preprint

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The development of precision agriculture has gradually introduced automation in the agricultural process to support and rationalize all the activities related to field management. In particular, service robotics plays a predominant role in this evolution by deploying autonomous agents able to navigate in fields while executing different tasks without the need for human intervention, such as monitoring, spraying and harvesting. In this context, global path planning is the first necessary step for every robotic mission and ensures that the navigation is performed efficiently and with complete field coverage. In this paper, we propose a learning-based approach to tackle waypoint generation for planning a navigation path for row-based crops, starting from a top-view map of the region-of-interest. We present a novel methodology for waypoint clustering based on a contrastive loss, able to project the points to a separable latent space. The proposed deep neural network can simultaneously predict the waypoint position and cluster assignment with two specialized heads in a single forward pass. The extensive experimentation on simulated and real-world images demonstrates that the proposed approach effectively solves the waypoint generation problem for both straight and curved row-based crops, overcoming the limitations of previous state-of-the-art methodologies.

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Design and Development of Intelligent Pesticide Spraying System for Agricultural Robot

Cited by 11 publications

References 16 publications

A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops

A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops

An Autonomous Pesticide Sprayer Robot with a Color-based Vision System

Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering

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