Automatic Arial Vehicle Based Pesticides Spraying System for Crops

Chavan*, Dr. Madhukar S.

doi:10.35940/ijitee.j9915.0981119

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…The use of remote control aerial application system (drone) (Martin et al, 2019) or UAV (Giles & Billing, 2015) (Figure 3k) is to be mostly used to perform pesticide spraying task specially in difficult F I G U R E 2 Framework of pesticide spraying robotic system category terrain due to its speed and strength (Chavan, 2019). It spray pesticide or fertilizers on crops without intervention of human beings.…”

Section: Platform Mobility and Steeringmentioning

confidence: 99%

“…But still observed that it is unable to provide complete protection to farmers and farmworkers. Generally, farmers used manual pesticides spraying devices like hand-operated sprayer (Bozdogan et al, 2000), battery-operated sprayer and they carry these devices on their shoulder which strains them (Chavan, 2019) and spray on the entire plant (Singh & Gupta, 2009) as shown in Figure 1a. Some farmers used self-propelled sprayer on-boarded on a tractor as shown in Figure 1b (Student & Nadu, 2019).…”

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confidence: 99%

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Pesticide spraying robot for precision agriculture: A categorical literature review and future trends

Meshram¹,

Vanalkar²,

Kalambe³

et al. 2021

Journal of Field Robotics

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Pesticide spraying is one of the risky and very essential task in agriculture to protect plants from harmful organisms. Many farmers are used manual pesticide spraying devices that they carry these devices on their shoulders and spray on the entire plant. Due to this, harmful pesticide chemicals spread into the air and may enter into the body of farmers and farm workers through the breathing system and mostly affect their body parts even, so many farmers lost their life. For this serious problem, we present the best solution to perform this task without the intervention of man is to utilize a robotic system in an agriculture. This paper analyses the state-of-the-art reviews in the development of autonomous pesticide spraying robots that are divided into four main categories; platform mobility and steering, localization and navigation control, sensing and target detection, and pesticide spraying arrangement. This paper also focused on the efficient methodology that can be used for future perspectives to enhance pesticide spraying operations in an agriculture. In this paper, to achieve target spraying operation, a suitable navigation control algorithm for platform movement and an efficient trajectory planning algorithm for shortest link movement are designed which covers the shortest distance to visit all targeted pests on the plant. This technique will help to reduce the overdose of toxic pesticides hence minimize the wastage of pesticides and also control its dangerous effects on the human being and on the environment.

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Section: Platform Mobility and Steeringmentioning

confidence: 99%

mentioning

confidence: 99%

Pesticide spraying robot for precision agriculture: A categorical literature review and future trends

Meshram¹,

Vanalkar²,

Kalambe³

et al. 2021

Journal of Field Robotics

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“…How to reasonably control weeds has become a major problem in agricultural development. At present, artificial pesticide spraying devices and drone spraying are the main ways to control weeds, which will have a bad impact on human body and environment, and even endanger human life [4][5][6][7][8] . Weed detection is the basis of intelligent weed control and intelligent equipment research [9][10][11] , and it is also the difficulty [12] .…”

Section: Introductionmentioning

confidence: 99%

Weed detection with Improved Yolov 7

Peng

Zhang

et al. 2023

EAI Endorsed Trans IoT

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INTRODUCTION: An improved Yolo v7 model.OBJECTIVES: To solve the weed detection and identification in complex field background.METHODS: The dataset was enhanced by online data enhancement, in which the feature extraction, feature fusion and feature point judgment of weed image were carried out by Yolov7 to predict the weed situation corresponding to the prior box. In the enhanced feature extraction part of Yolov7, CBAM, an attention mechanism combining channel and space, is introduced to improve the attention of the algorithm to weeds and strengthen the characteristics of weeds.RESULTS: The mean average precision (mAP ) of the improved algorithm reached 91.15%, which was 2.06% higher than that of the original Yolov7 algorithm. Compared with the current mainstream target detection algorithms Yolox, Yolov5l, Fster RCNN, Yolov4-tiny and Yolov3, the mAP value of the improved algorithm increased by 4.35, 4.51, 5.41, 19.77 and 20.65 percentage points. Weed species can be accurately identified when multiple weeds are adjacent.CONCLUSION: This paper provides a detection model based on Yolov7 for weed detection in the field, which has a good detection effect on weed detection, and lays a research foundation for intelligent weeding robot and spraying robot.

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