Plant Disease Detection based on Deep Learning Approach

Akhtar, Faizan; Partheeban, N.; Daniel, A. K.; Sriramulu, Srinivasan; Mehra, Saloni; Gupta, Nishant

doi:10.1109/icacite51222.2021.9404647

Cited by 6 publications

(1 citation statement)

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“…They employed the YOLOv5 model to identify the plant diseases accurately, with an accuracy of 86-87 %. A method for achieving plant disease detection through the use of ConvNets to create a CNN was proposed by Marwan Adnan Jasim et al [5]. Through processing the leaf, these earlier studies and research attempts studying plant disease detection were able to identify the diseases in the plant.…”

Section: IImentioning

confidence: 99%

Plant Disease Detection Using YOLOv7 Algorithm

Praveen,

Sravani,

Akhil

et al. 2024

IJRASET

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Plant diseases pose a serious danger to global food security and can result in huge financial losses for the agricultural sector. Earlier Plant disease detection and precise diagnosis are essential for putting management measures into place. Recent advancements in computer vision techniques have demonstrated encouraging outcomes in automating activities related to illness identification. This study uses the You Only Look Once (YOLOv7) object identification algorithm to present a novel method for plant disease diagnosis. The primary goal of this research is to create a reliable and effective system that can quickly and reliably identify plant diseases. YOLOv7, a highly accurate and speedy algorithm, will serve as the main underpinning for detection. The project's primary goal is to train the Yolov7 model to identify distinct citrus plant illnesses by using a large dataset that includes pictures of both healthy and diseased plants. This project categorizes leaf images recorded from a file or webcam into four categories: healthy, greening, blackspot, and canker. Early disease prediction allows farmers to take required security measures for their plants.

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Section: IImentioning

confidence: 99%