Plant Leaf Disease Detection And Classification Based On Machine Learning Model

Aashish Jha, Madhavi Purohit, Vivek Maurya, Amiyakumar Tripathy
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引用次数: 1

Abstract

Many industries today have benefited from developing new technologies, particularly data science, machine learning, artificial intelligence, and deep learning. This includes agriculture. Previous research have shown that plant leaf diseases are losing productivity at an increasing pace, which accounts for 40-42% of agricultural production losses (Cost: 12.42 billion euros; Source: United Nations Food and Agriculture Organization (FAO)). This big issue may be resolved by employing this method for recognizing plant leaf disease from the input photographs. This technique involves steps including feature extraction, image segmentation, and image preprocessing. Next, a convolutional neural network-based classification approach is applied. The suggested implementation was 98.3% accurate in predicting plant leaf diseases.
基于机器学习模型的植物叶片病害检测与分类
如今,许多行业都受益于新技术的发展,尤其是数据科学、机器学习、人工智能和深度学习。这包括农业。先前的研究表明,植物叶片病害正在以越来越快的速度丧失生产力,占农业生产损失的40-42%(成本:124.2亿欧元;资料来源:联合国粮食及农业组织(粮农组织)。利用该方法从输入的照片中识别植物叶片病害,可以解决这一重大问题。该技术涉及的步骤包括特征提取、图像分割和图像预处理。接下来,应用基于卷积神经网络的分类方法。该方法预测植物叶片病害的准确率为98.3%。
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