Specification development of robotic system for pesticide spraying in greenhouse

Komašilovs, Vitālijs; Stalidzāns, Egils; Osadčuks, Vitālijs; Mednis, Martins

doi:10.1109/cinti.2013.6705239

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…Komasilovs et al (2013) used a GA called GAMBot-Eva to optimize the design of a robotic system traveling through the greenhouse layout, evaluating health conditions of plants, and spraying pesticides on them if needed. The optimization problem took into account tasks of the robot, the price, and the energy consumption of the robot components.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies

Cio

Vadean

et al. 2020

AIEDAM

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The development of autonomous greenhouses has caught the interest of many researchers and industrial considering their potential of offering an optimal environment for the growth of high-quality crops with minimum resources. Since an autonomous greenhouse is a mechatronic system, the consideration of its subsystem (e.g. heating systems) and component (e.g. actuators and sensors) interactions early in the design phase can ease the product development process. Indeed, this consideration could shorten the design process, reduce the number of redesign loops, and improve the performance of the overall mechatronic system. In the case of a greenhouse, it would lead to a higher quality of the crops and a better management of resources. In this work, the layout design of a general autonomous greenhouse is translated into an optimization problem statement while considering product-related dependencies. Then, a genetic algorithm is used to carry out the optimization of the layout design. The methodology is applied to the design of a fully autonomous greenhouse (45 cm × 30 cm × 30 cm) for the growth of plants in space. Although some objectives are conflictual, the developed algorithm proposes a compromise to obtain a near-optimal feasible layout design. The algorithm was also able to optimize the volume of components (occupied space) while considering the energy consumption and the overall mass. Their respective summed values are 2844.32 cm3, which represents 7% of the total volume, 5.86 W, and 655.8 g.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies

Cio

Vadean

et al. 2020

AIEDAM

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“…Corn needs to be weeded and dewormed during planting. The traditional way is to achieve the purpose of weeding and deworming through artificial spraying, but this way is obviously inefficient in large‐scale corn planting fields (Komasilovs et al, 2013). With the rapid development of smart agriculture, corn spraying robots play an increasingly important role in the corn planting process.…”

Section: Introductionmentioning

confidence: 99%

Navigation line extraction algorithm for corn spraying robot based on YOLOv8s‐CornNet

Guo,

Diao,

Zhao

et al. 2024

Journal of Field Robotics

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The continuous and close combination of artificial intelligence technology and agriculture promotes the rapid development of smart agriculture, among which the agricultural robot navigation line recognition algorithm based on deep learning has achieved great success in detection accuracy and detection speed. However, there are still many problems, such as the large size of the algorithm is difficult to deploy in hardware equipment, and the accuracy and speed of crop row detection in real farmland environment are low. To solve the above problems, this paper proposed a navigation line extraction algorithm for corn spraying robot based on YOLOv8s‐CornNet. First, the Convolution (Conv) module and C2f module of YOLOv8s network are replaced with Depthwise Convolution (DWConv) module and PP‐LCNet module respectively to reduce the parameters (Params) and giga floating‐point operations per second of the network, so as to achieve the purpose of network lightweight. Second, to reduce the precision loss caused by network lightweight, the spatial pyramid pooling fast module in the backbone network is changed to atrous spatial pyramid pooling faster module to improve the accuracy of network feature extraction. Meanwhile, normalization‐based attention module is introduced into the network to improve the network's attention to corn plants. Then the corn plant was located by using the midpoint of the corn plant detection box. Finally, the least square method is used to extract the corn crop row line, and the middle line of the corn crop row line is the navigation line of the corn spraying robot. From the experimental results, it can be seen that the navigation line extraction algorithm proposed in this paper ensures both the real‐time and accuracy of the navigation line extraction of the corn spraying robot, which contributes to the development of the visual navigation technology of agricultural robots.

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“…Moreover, in the greenhouses, autonomous pesticide robots are interested in researchers because of the cost reduction of this operation [8][9][10][11][12]. In addition to cost reduction, the application of robots in this field helps to keep clean the environment of the greenhouse without human operators [13][14][15].…”

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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Specification development of robotic system for pesticide spraying in greenhouse

Cited by 12 publications

References 12 publications

Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies

Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies

Navigation line extraction algorithm for corn spraying robot based on YOLOv8s‐CornNet

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

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