Tomato And Potato Leaf Disease Prediction With Health Benefits Using Deep Learning Techniques

K. Karthik, S. Rajaprakash, S. Nazeeb Ahmed, Rishan Perincheeri, C. R. Alexander
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引用次数: 1

Abstract

The main challenge to the farmers is that the weather, environmental factors cannot be predicted and controlled. Plant diseases also plays an important role in plant cultivation. Plant diseases are considered to be a major challenge to the farmers. As plant and leaf diseases is difficult to be identified with the naked eyes. To overcome this issue in the existing approach, the farmers periodically spray pesticides which might spoil the plants, crop failure. Thus, effective monitoring and identification of plant leaf disease detection at the early stage is essential to predict the leaf diseases and recommend preventive measures. The proposed system utilizes image processing with deep learning techniques to detect plant leaf diseases from potato and tomato datasets. Also, our proposed system could able to recommend the plant benefits helping the current generation of people with a common knowledge base along with plant leaf diseases prediction. For experimental results, this research uses jupyter tool with python script for performing plant leaf disease analysis.
利用深度学习技术预测番茄和马铃薯叶病对健康有益
农民面临的主要挑战是天气、环境因素无法预测和控制。植物病害在植物栽培中也起着重要的作用。植物病害被认为是农民面临的主要挑战。由于植物和叶片病害是难以用肉眼识别的。在现有的方法中,为了克服这个问题,农民定期喷洒农药,这可能会破坏植物,导致作物歉收。因此,对植物叶片病害进行早期有效的监测和鉴定,对预测叶片病害并提出预防措施至关重要。该系统利用图像处理和深度学习技术从马铃薯和番茄数据集中检测植物叶片病害。此外,我们提出的系统可以推荐植物的益处,帮助当代人拥有共同的知识库以及植物叶片疾病预测。对于实验结果,本研究使用jupyter工具和python脚本进行植物叶片病害分析。
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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