AI-based Detection of Pest Infected Crop and Leaf

Mustafa Ahmed, T. Mahajan, Bhupender Datt Sharma, Mahendra Kumar, S. Singh
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引用次数: 1

Abstract

Plant infection/disease is one of the ongoing challenges for farmers, which imposes a threat on their income and food security. Detecting infection in plants or crops is an onerous task because the analysis of each crop in large fields takes too much time, effort, work force and expertise. In this paper, we have proposed AI-based detection of pest infected crop and leaf. The proposed method is helpful in analyzing crops in a time-efficient manner and gives more accurate results. The classification of the crops is done on the basis of their images. A publicly available dataset is used to analyze the proposed methodology. Image processing methods are used in order to analyze the crops, further convolutional neural networks are applied to differentiate the healthy crops from the ones that are infected from some disease and also show some visual remarks.
基于人工智能的病虫害作物及叶片检测
植物感染/病害是农民面临的持续挑战之一,对农民的收入和粮食安全构成威胁。检测植物或作物的感染是一项繁重的任务,因为分析大片土地上的每种作物需要太多的时间、精力、劳动力和专业知识。本文提出了一种基于人工智能的病虫害作物和叶片检测方法。该方法有助于提高作物分析的时效性和准确性。农作物的分类是在其图像的基础上完成的。使用公开可用的数据集来分析所提出的方法。利用图像处理方法对作物进行分析,进一步利用卷积神经网络对健康作物和受病害影响的作物进行区分,并给出一些视觉标记。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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