利用深度学习改进植物叶片病害检测和分类方法

Jeetendra Mahor, Ashish Gupta
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摘要

在印度这样以农业为重要职业的国家,农作物一旦受到任何类型疾病的影响,就会面临巨大损失。这些疾病在不同阶段侵袭农作物,并可能摧毁整个生产。由于大多数病害都是从一种作物传染到另一种作物的,因此必须及早发现病害类型,以便农民采取必要行动 "挽救作物 "和生产。早期发现病害是提高农业生产率的基本活动之一。病害会在影响植物生长的叶片部位迅速传播。早期检测是一项具有挑战性的任务,因为症状轻微,难以准确识别。本研究论文介绍了一种基于增强型 CNN 的 MCC-ECNN 模型,该模型具有微调超参数和各种批量大小,可用于准确的植物叶片病害分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Improve Method for Plant Leaf Disease Detection and Classification using Deep Learning
In countries like India, whose important occupation is agriculture, face a huge loss when the crops get affected by any type of disease. These diseases attack the crops in various stages and can destroy the entire production. Since most diseases are transmitted from one crop to another there is an essential requirement to detect the type of disease in the early stage so that farmers can take the required action to “save the crops” and production. Early disease detection is one of the essential activities for enhancing agricultural productivity. Diseases spread very quickly in the parts of the leaves that affect the growth of the plants. Early detection is a challenging task as the symptoms are mild for accurate identification. This research paper presents an enhanced CNN based MCC-ECNN model with fine-tuned hyper-parameters and various batch sizes for accurate plant leaf disease classification.
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