Plant and Soil ( IF 3.9 ) Pub Date : 2022-04-29 , DOI: 10.1007/s11104-022-05407-3 Anju Yadav 1 , Udit Thakur 1 , Rahul Saxena 1 , Vipin Pal 2 , Vikrant Bhateja 3, 4 , Jerry Chun-Wei Lin 5
|
Background
Plant diseases significantly affect the crop, so their identification is very important. Correct identification of these diseases is crucial for establishing a good disease control strategy to avoid time and financial losses. In general, machines can greatly reduce the possibility of human error. In particular, computer vision techniques developed through deep learning have paved a way to detect and diagnose these plant diseases on the leaf.
Methods
In this work, the model AFD-Net was developed to detect and identify various leaf diseases in apple trees. The dataset is from Kaggle 2020 and 2021 and was financially supported by the Cornell Initiative for Digital Agriculture. An AFD-Net was proposed for leaf disease classification in apple trees and the results of the efficiency of the model are compared with other state-of-the-art deep learning approaches.
Results
The results of the experiments in the validation dataset show that the proposed AFD-Net model achieves the highest values of 98.7% accuracy for Plant Pathology 2020 and 92.6% for Plant Pathology 2021 compared to other deep learning models in the original and extended datasets.
Discussion
The results also indicate the efficiency of the proposed model in identifying leaf diseases on apple trees for major and minor classes, i.e., for multiple classification.
中文翻译：
AFD-Net：在植物病理学数据集上使用深度学习的苹果叶面病多分类
背景
植物病害显着影响作物，因此它们的识别非常重要。正确识别这些疾病对于建立良好的疾病控制策略以避免时间和经济损失至关重要。一般来说，机器可以大大降低人为错误的可能性。特别是，通过深度学习开发的计算机视觉技术为检测和诊断叶片上的这些植物病害铺平了道路。
方法
在这项工作中，开发了模型 AFD-Net 来检测和识别苹果树中的各种叶片病害。该数据集来自 Kaggle 2020 和 2021，并得到康奈尔数字农业倡议的财政支持。提出了一种用于苹果树叶病分类的 AFD-Net，并将该模型的效率结果与其他最先进的深度学习方法进行了比较。
结果
验证数据集中的实验结果表明，与原始数据集中和扩展数据集中的其他深度学习模型相比，所提出的 AFD-Net 模型在 Plant Pathology 2020 和 Plant Pathology 2021 中的准确率分别为 98.7% 和 92.6%。
讨论
结果还表明，所提出的模型在识别主要和次要类别（即多重分类）的苹果树叶片病害方面的效率。
京公网安备 11010802027423号