Drone Assisted Effective Pesticide Sprayer

Vandana, C P; Prakash, Anuj; Bhowal, Arnab; D, Monisha Taj

doi:10.32628/cseit206381

Cited by 1 publication

(1 citation statement)

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“…The stuffed pepper was detected with a drone using the YOLOv3 model for pesticide spraying. The developed model has been tested in real time on the Raspberry Pi development board (Vandana et al, 2020). Khan et al (2021aKhan et al ( , 2021bKhan et al ( , 2021c used the faster R-CNN model to determine the areas to be sprayed with a drone.…”

Section: Discussionmentioning

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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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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