Plant Disease Detection using Ensembled CNN Framework

Subhash Mondal, Suharta Banerjee, Subinoy Mukherjee, Diganta Sengupta
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Abstract

Agriculture exhibits the prime driving force for growth of agro-based economies globally. In the field of agriculture, detecting and preventing crops from attacks of pests is the major concern in today's world. Early detection of plant disease becomes necessary to prevent the degradation in the yield of crop production. In this paper, we propose an ensemble based Convolutional Neural Network (CNN) architecture that detects plant disease from the images of the leaves of the plant. The proposed architecture takes into account CNN architectures like VGG-19, ResNet-50, and InceptionV3 as its base models, and the prediction from these models is used as an input for our meta-model (Inception-ResNetV2). The approach helped us in building a generalized model for disease detection with an accuracy of 97.9 % under test conditions.
利用集成CNN框架进行植物病害检测
农业是全球农业经济增长的主要动力。在农业领域,检测和预防作物的虫害是当今世界关注的主要问题。早期发现植物病害对于防止作物产量下降是必要的。在本文中,我们提出了一种基于集成的卷积神经网络(CNN)架构,从植物叶片图像中检测植物病害。提出的体系结构考虑了CNN体系结构,如VGG-19、ResNet-50和InceptionV3作为其基本模型,来自这些模型的预测被用作我们的元模型(Inception-ResNetV2)的输入。该方法帮助我们建立了一个广义的疾病检测模型,在测试条件下准确率为97.9%。
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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