Original article | Journal of Biometry Studies 2022, Vol. 2(2) 48-56
Kazi Riad UDDIN, Md Habib KHAN, Mohammad Mizanur RAHMAN, Arafath Al FAHIM
pp. 48 - 56 | DOI: https://doi.org/10.29329/JofBS.2022.445.02 | Manu. Number: MANU-2209-19-0002.R2
Published online: December 29, 2022 | Number of Views: 48 | Number of Download: 278
The preliminary identification of plant diseases plays a predominant role in preventing loss of production. The laboratory identification process of plant diseases is time-consuming and could not be conducted in the countryside, where experiment facilities are rarely found. This paper shows a deep learning approach to confine the infection area and identify the diseases by using images of their leaves. Deep learning works well with large amounts of data. So we can increase the accuracy and reduce the loss by engrossing a plethora of data. However, it will not increase the efficiency of the models. In this paper, we use several cutting-edge deep learning models, such as MobileNet, ResNet, and EfficientNet, along with Faster R-CNN and SSD on a small dataset. The dataset contains 2366 images of 27 types. The dataset was taken in a real environment. The data augmentation technique cannot be used with a small dataset. All state-of-the-art deep learning model are trained as a baseline to work on the efficiency of the models. We experiment with the best performer for computation cost. So, to increase the efficiency of the model we implement cyclic learning rate which performs 53.81% map@.50 on best performer EfficientDet. It also lessens the variance, which suggests that cyclic learning not only works as a learning rate but also functions as a data augmentation. In the future, we will apply this learning rate to a dataset containing a large number of plant disease collection images, where different types of data augmentation can be used to not only increase the images but also decrease the generalization loss. Farmers can predict plant diseases more accurately using this system.
Keywords: PlantDoc, Deep learning, Convolution neural network, Cyclic learning rate
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