International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 02 | Feb 2024
p-ISSN: 2395-0072
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Explainable AI(XAI) using LIME and Disease Detection in Mango Leaf by Transfer Learning Approach Vishakha Mistry Head of Department, Department of Information Technology, 360 Research Foundation, Tumkaria, Bihar, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Agriculture is an essential means of earning
learning frameworks when the output of the LIME model is visually represented.
income for a significant percentage of the worldwide population. As a result, the productivity of crops has become crucial all over the world. Farmers will get more benefits from using modern digital tools for autonomous disease detection. Because agriculture is a complex field, it is particularly essential to improve the interpretability of agricultural ML models. First, the article proposes to identify leaf disease in Mango plants via pre-trained deep-learning architecture. Secondly, for the purpose of demonstrating the interpretability of my model's choice, I made use of Local Interpretable ModelAgnostic Explanations (LIME), an explainable AI (XAI) tool.
2. LITERATURE SURVEY Several academics offered numerous machine learning (ML) and deep learning (DL) methodologies for detecting various diseases in plant leaves. Adi Dwifana Saputra , Djarot Hindarto, Handri Santoso [2] have presented rice leaves disease classification using the Convolutional Neural Network algorithm with DenseNet architecture. The accuracy of DenseNet211 was 91.67%, that of DenseNet169 was 90%, and that of DenseNet201 was 88.33%. The training duration of the model was 24 seconds.
Key Words: leaf disease detection, Transfer learning, explainable Artificial Intelligence (XAI), Local Interpretable Model-agnostic Explanations (LIME).
Authors in [3] have used PlantVillage and PlantDoc dataset to identify leaf disease in the corn plant. EfficientNetB0 architecture was used in this research article. The performance of the proposed architecture is compared with Inception V3, VGG16, Resnet50, Resnet101, and Densenet121. The proposed approach achieved an accuracy of 98.85% and a precision of 88% and it is more computationally efficient.
1. INTRODUCTION Artificial intelligence and machine learning are now used in diverse agricultural applications. The mango fruit tree is a highly cultivated crop that is economically important across the majority of the world. In general, it is highly prized because of its beneficial nutrient content and mouthwatering taste, and it plays an important part in the lives of millions of farmers. However, this crop is susceptible to a variety of illnesses, which can reduce the yield and overall quality of the plants. The quick identification of these diseases in mango plants is needed to prevent the spread of disease. The original method for identifying mango leaf disease was visual examination by agricultural professionals. Which has time commitment and knowledge-dependent limitations. Automatic leaf disease identification is accomplished via the use of machine learning and computer vision disciplines.
H. Amin et al. [4] have used two pre-trained CNN architectures namely EfficientNetB0 and DenseNet121. The Authors have applied feature fusion techniques features extracted from two models. The proposed model achieved 98.56% accuracy which is the highest amongst Resnet152, InceptionV3, and Densenet121. Authors in [5] have performed classification of leaf disease on different fruit leaves. The average accuracy of VGG, GoogLeNet and ResNet are compared and ResNet have shown best accuracy amongst all. Explainability testing is done using GradCAM, LIME, and SmoothGrad techniques on convolution-based neural networks.
The goal of this research is to identify mango leaf disease using a pre-trained transfer learning-based machine learning system. Model interpretability must also be researched as the number of convolution neural networks grows and the framework black box interpretability problem becomes more relevant. In order to make the models more transparent and interpretable, explanatory artificial intelligence (XAI) reveals its importance, which is considered to be at the highest level of explainability, accuracy, and performance [1]. Researchers are better able to comprehend the reasoning behind the outcomes of deep
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3. DATASET DESCRIPTION My dataset was gathered from Kaggle. The dataset includes pictures of 32 different types of Indian mango leaves. The collection includes 768 photos of 32 Indian mango leaf species, with 24 photos of each species taken at various orientations and angles. The dataset's sample images are seen in Figure 1.
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