人工智能
卷积神经网络
计算机科学
机器学习
植物病害
随机森林
决策树
深度学习
F1得分
人工神经网络
农业
任务(项目管理)
模式识别(心理学)
工程类
生物技术
生物
系统工程
生态学
作者
Barsha Biswas,Rajesh Kumar Yadav
标识
DOI:10.1109/inocon57975.2023.10101125
摘要
Around 38% of land in the world is used for agriculture and the whole world is completely dependent on agriculture. So, that’s why good crop yield is very important to get high agricultural output. A single disease in a plant can lower crop yield. So, to maintain a high agricultural output, we need to detect disease at the early stage so that the agricultural output should be maintained. There are multiple ways to detect plant disease like detecting a plant disease by using the naked eye by hiring an expert, or by using Artificial Intelligence (AI). By using AI, it takes less time to detect plant disease as compared to detecting using the naked eye. Deep Learning (DL), the sub-branch of AI gives an accurate result as compared to the other sub-branches of AI. In DL, Convolutional Neural Network or CovNet is the latest and revolutionary algorithm to perform this task. An apple tree disease detection model, based on Multilayer CNN, is presented in the paper. To train the proposed Multilayer CNN model, the data is collected from FGVC8 dataset from Plant Pathology 2021, a Kaggle Competition which is supported by the “Cornell Initiative for Digital Agriculture Decision Trees, Logistic Regression, and Random Forests are machine learning algorithms that are compared with the performance of the proposed model. This study shows that the proposed model outperforms Machine Learning algorithms with the accuracy of 91%, Precision of 89%, Recall of 85% and F1-Score of 88.34%.
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