Performance Analysis of Convolutional Neural Network for Plant Diseases Identification

Lohith R, Manjula R. Bharamagoudra, T. S. S. Reddy, K. Sravani
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Abstract

Our daily life starts with providing nutrition to our human body. A huge amount of food is provided by the agricultural sector. But always there isn’t 100% yield because of some issues like plant diseases, irregular rainfall, Natural disasters, etc. A major issue is plant diseases which are troublesome for this industry. An accurate and quick detection model is required for identifying the disease. In this paper, we have tested many classification algorithms for performance analysis such as EffecientNet-B0, GoogleNet, Resnext50 32x4d, and MobileNet-V2 on a GPU system. Various parameters have been taken into consideration for evaluating different classification models such as training time, training accuracy, and total loss to predict the best model which uses the least GPU cores and the result claims that Resnext50 32x4d gives higher accuracy.
卷积神经网络在植物病害识别中的性能分析
我们的日常生活从为人体提供营养开始。农业部门提供了大量的粮食。但由于植物病害、不规则降雨、自然灾害等问题,总不能达到100%的产量。一个主要的问题是困扰这个行业的植物病害。需要一个准确、快速的检测模型来识别疾病。在本文中,我们在GPU系统上测试了许多用于性能分析的分类算法,如EffecientNet-B0、GoogleNet、Resnext50 32x4d和MobileNet-V2。在评估不同的分类模型时,考虑了各种参数,如训练时间、训练精度和总损失,以预测使用最少GPU内核的最佳模型,结果表明Resnext50 32x4d给出了更高的精度。
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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