农业植物病害自动检测

A. Afonin, Kyrylo Kundik
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引用次数: 0

摘要

机器学习技术近年来发展迅速,人们现在可以在生活的各个领域使用它们,使他们的生活更轻松,更美好。农业产业也不落后,每年都有越来越多的问题在机器学习算法的帮助下得到解决。然而,尚未解决的问题之一是农业植物病害的识别问题。根据联合国的研究,全世界每年约有40%的农作物死于各种疾病,其中大部分可以通过及时干预和治疗来避免。为了解决这个问题,我们为每个人提供了一个简单易用的服务,它将允许人们通过植物叶子的图像来预测它是生病还是健康,或者它是否需要任何帮助或入侵。这项服务对于从事农作物种植的小农场来说是必不可少的。因此,这将使这些企业的员工能够立即发现疾病,并收到对他们重要的植物的护理建议。因此,决定开发一个神经网络架构来解决这个问题:通过叶子的图像来预测植物的疾病。这种神经网络模型是轻量级的,不需要花费太多的时间来学习,并且在我们的数据集上具有很高的准确性。本文还研究了哪些流行的深度神经网络架构(如XceptionNet、DenseNet等)在解决这一问题时具有很高的准确性。为了实现最终用户(即农民)使用该模型的可能性,决定以电报机器人的形式开发一种特殊的web服务。有了这个机器人,任何人都可以上传农业植物的叶子图像,并检查这些植物是否健康或没有任何疾病。这个机器人还经过训练,可以就疾病的治疗或健康植物的适当栽培向园丁提供适当的建议。这一解决方案完全解决了问题,完全有可能成为保护世界粮食收成不可或缺的帮手。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Determination of Agricultural Plant Diseases
Machine learning technologies have developed rapidly in recent years, and people are now able to use them in various spheres of life, making their lives easier and better. The agro-industry is not lagging behind, and every year more and more problems in this area are solved with the help of machine learning algorithms. However, among the problems that have not yet been solved is the problem of identifying diseases of agricultural plants. According to the UN research, about 40% of the world’s harvest dies each year from various diseases, most of which could be avoided through timely intervention and treatment.To solve this problem, we offer an easy, accessible service for everyone, which will allow one to predict by the image of the plant leaves whether it is sick or healthy, or whether it needs any help or intrusion. This service will be indispensable for small farms engaged in growing crops. Thus, it will allow employees of such enterprises to immediately detect diseases and receive recommendations for the care of plants important to them.Therefore, it was decided to develop a neural network architecture that will solve this problem: the prediction of a plant disease by the image of its leaves. This neural network model is lightweight, does not take much time to learn, and has high accuracy on our dataset. It was also investigated which popular architectures (e.g. XceptionNet, DenseNet, etc.) of deep neural networks can have great accuracy in solving this problem. To realize the possibility of using the model by end users, i.e. farmers, it was decided to develop a special web service in the form of a telegram bot. With this bot, anyone can upload images of the leaves of agricultural plants and check whether this plant is healthy or free of any diseases. This bot is also trained to give appropriate advice to gardeners on the treatment of diseases or the proper cultivation of healthy plants.This solution fully solves the problem and has every chance to become an indispensable helper in preserving the world harvest.
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