Plant Disease Prediction using Transfer Learning Techniques

A. Lakshmanarao, N. Supriya, A. Arulmurugan
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Abstract

Plant diseases are a significant hazard to feed a growing population, but due to a lack of infrastructure in many regions of the world, timely detection is challenging. Finding and detecting plant illness is essential in agricultural production. It takes a great deal of time and effort to find the disease. Agricultural sector can also reap the benefits of machine learning and deep learning. There has been a recent rise in the use of ML & DL techniques in plant disease identification. In this paper, we applied transfer learning technique for plant disease prediction. We used a ‘plantvillage’ dataset collected from Kaggle. Images of fifteen different types of plant leaves (Tomato, Potato, Pepper bell), from three distinct plants are included in this collection. We split the original dataset into three parts for three different plants and applied three transfer learning techniques VGG16, RESNET50, Inception and achieved accuracy of 98.7%, 98.6%, 99% respectively. The results of experiments shown that our proposed model achieved good accuracy when compared to traditional models.
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利用迁移学习技术进行植物病害预测
植物病害是养活不断增长的人口的重大危害,但由于世界许多地区缺乏基础设施,及时发现病害具有挑战性。发现和检测植物病害在农业生产中至关重要。发现这种疾病需要花费大量的时间和精力。农业部门也可以从机器学习和深度学习中获益。最近在植物病害鉴定中使用ML和DL技术有所增加。本文将迁移学习技术应用于植物病害预测。我们使用了从Kaggle收集的“plantvillage”数据集。十五种不同类型的植物叶片(番茄,土豆,胡椒钟)的图像,从三个不同的植物包括在这个集合。我们将原始数据分成三部分,分别针对三种不同的植物,应用了三种迁移学习技术VGG16、RESNET50、Inception,准确率分别达到了98.7%、98.6%、99%。实验结果表明,与传统模型相比,本文提出的模型具有较好的精度。
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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