Computer Vision based Plant Disease Detection using Machine Learning Technique

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Abstract

Our lives would be incomplete without agriculture. As a result, disease identification is crucial in agriculture since untreated diseases cause many plants to go extinct. Since it is always visible and available, the leaf picture was chosen for detecting purposes. In addition to helping plants develop quickly, leaves also help plants produce more food. The suggested system may identify plant leaf disease by using an image of the leaf as the source data. In order to train computers and produce accurate predictions, AI systems also require a large amount of data. The diagnosis and detection of plant leaf disease is a crucial component of an AI-based system model for the agricultural sector. In this paper, performance-improving machine learning algorithms are presented for image-based plant leaf disease diagnosis. Python version 3.7 is used to run the simulation. The total accuracy for identifying various plant leaf diseases is 98%
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基于机器学习技术的植物病害计算机视觉检测
没有农业,我们的生活将是不完整的。因此,疾病鉴定在农业中至关重要,因为未经治疗的疾病会导致许多植物灭绝。由于叶子图像总是可见和可用的,因此选择叶子图像进行检测。除了帮助植物快速生长,叶子还能帮助植物生产更多的食物。所建议的系统可以通过使用叶片图像作为源数据来识别植物叶片病害。为了训练计算机并产生准确的预测,人工智能系统还需要大量的数据。植物叶片病害的诊断和检测是农业部门基于人工智能的系统模型的关键组成部分。本文提出了一种基于图像的植物叶片病害诊断机器学习算法。Python 3.7版本用于运行模拟。鉴定各种植物叶片病害的总准确率为98%
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