Design and development of an Hexacopter for fertlizer spraying in agriculture fields

Susitra, D.; Jebaseeli, E. Annie Elisabeth; Chitturi, Venkata Kishore; Chadalavada, Vineet

doi:10.1088/1742-6596/1706/1/012053

Cited by 6 publications

(8 citation statements)

References 5 publications

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“…The produced drone of Uz (2019) had 9 min of flight time. In addition to these studies, Garre and Harish (2018) the quadcopter model with a 5-L pesticide capacity (Susitra et al , 2020) produced a hexacopter model with a 1 kg payload capacity and a flight time of 15 min. This study created a drone system with a hexacopter model that can fly for 12 min with 5 L of pesticide capacity for spraying purposes.…”

Section: Discussionmentioning

confidence: 99%

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Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Akdoğan,

Özer,

Oğuz

2024

RIA

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Purpose Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV). Design/methodology/approach Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV. Findings In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%. Originality/value An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

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Section: Discussionmentioning

confidence: 99%

“…Brushless motors contain permanent magnets instead of brushes (Desale et al , 2019). So, they cannot be operated directly and need a driver circuit (Susitra et al , 2020). The ESCs produce the control signals to control the speed of the motors (Mhetre et al , 2020).…”

Section: Materials Methodsmentioning

confidence: 99%

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Akdoğan,

Özer,

Oğuz

2024

RIA

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“…The distribution and deposition of droplets in the coffee plant canopy were significantly affected by the operational heights of flight. The applications of droplets, which were sprayed close to the coffee plant canopy, had little or no effect from wind that could cause a significant drift; however, the chance that the sprayed droplets could be affected by height increased as the spraying height was increased [11,[24][25][26].…”

Section: Discussionmentioning

confidence: 99%

Droplet Deposition of Leaf Fertilizers Applied by an Unmanned Aerial Vehicle in Coffea Canephora Plants

et al. 2023

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The yield of coffee plants depends on several factors, and the leaf nutrition is one of the most important. The form of application of leaf fertilizers used is expensive considering the application efficacy and the efficacy of adherence and absorption of nutrients. The objective of this work was to evaluate the effect of configurations of operational parameters of centrifugal nozzle rotation and operational height on the application efficacy and efficacy of absorption of leaf fertilizers in Conilon coffee plants. The coverage, density, and deposition of spray droplets were efficacy parameters evaluated through water-sensitive paper tags and artificial targets. The efficacy of the macronutrients applied was confirmed through leaf analyses at 15 and 30 days after application. The application efficacy with a height of flight of 2.0 m and rotation of 10,000 rpm was the most adequate, with coverage of 12.4% and density of droplets of 127 droplets cm−2. The application of foliar fertilizers by UAV provided a distribution in newer leaves in which the absorption of macronutrients was greater, and the flight height of 2.0 m and rotation of the centrifugal nozzle of 10,000 rpm resulted in the best efficacy parameters.

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“…Research on UAV structures, development of several sensors, and novelty in spot region spraying along with the application of artificial intelligence (AI) and deep learning for distant agricultural monitoring is presented in Hafeez et al (2022). Despite the availability of commercial drones, there are several ongoing research studies related to the deployment of semi-automatic methods and land-specific systems for precise spraying (Hanif et al, 2022;Susitra et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Despite the availability of commercial drones, there are several ongoing research studies related to the deployment of semi-automatic methods and land-specific systems for precise spraying (Hanif et al. , 2022; Susitra et al. , 2020).…”

Section: Introductionmentioning

confidence: 99%

Design of hexacopter and finite element analysis for material selection

Raut,

Jadhav,

Jadhav

2023

IJIUS

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PurposeThe purpose of this study is to offer a better and more effective hexacopter design for a 3 kg payload using finite element analysis (FEA), facilitating the use of different materials for different components that too without compromising strength.Design/methodology/approachA 3D computer-aided design (CAD) model of a hexacopter with a regular hexagonal frame is presented. Furthermore, a finite element model is developed to perform a structural analysis and determine Von Mises stress and strain values along with deformations of different components of the proposed hexacopter design.FindingsThe results establish that carbon fibre outperforms acrylonitrile butadiene (ABS) with respect to deformations. Within the permissible limits of the stress and strain values, both carbon fiber and ABS are suggested for different components. Thus, a proposed hexacopter offers lighter weight, high strength and low cost.Originality/valueThe use of different materials for different components is suggested by making use of static structural analysis. This encourages new research work and helps in developing new applications of hexacopter, and it has never been reported in literature. The suggested materials for the components of the hexacopter will prove to be suitable considering weight, strength and cost.

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Design and development of an Hexacopter for fertlizer spraying in agriculture fields

Cited by 6 publications

References 5 publications

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Droplet Deposition of Leaf Fertilizers Applied by an Unmanned Aerial Vehicle in Coffea Canephora Plants

Design of hexacopter and finite element analysis for material selection

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