Plant Disease Detection Using Machine Learning

Abstract

In this project, we have developed a plant disease detection model using a machine learning approach on a dataset named “Plant Disease Expert” which is available on Kaggle. The dataset contains 199,611 images across 58 subdirectories. The splitting of the dataset in training, validation and testing is done in ratio 90:5:5. The data preprocessing included resizing the images, normalizing pixel values and applying augmentation techniques. We have used different libraries throughout the project such as NumPy, Pandas, Operating system, Time, Matplotlib, OpenCV, Shutil, TensorFlow, Keras, Seaborn, etc. EfficientNetB3 is used as the base model. EfficientNetB3 is a convolutional neural network architecture developed by Google Brain researchers [1]. It is known for its efficiency and effectiveness in image classification tasks. Additional custom layers such as max pooling, batch normalization, dense layers, and activation functions are used to optimize the performance. The adamax optimizer and categorical cross-entropy were used during training. The model was trained for 10 epochs with a batch size of 20 and a learning rate of 0.01. For evaluation, classification report, accuracy, precision, recall, F1-score and support were used. The model achieved an accuracy of 98.76% [1].

Plant phenotyping relevance

植物画像から病害状態を推定する機械学習モデルの開発・評価が研究の中心であり、植物病害の画像ベース表現型解析に該当します。

Code and data availability

The paper's plant-phenotyping analysis is built entirely on a public image dataset, 'Plant Disease Expert' by S. S. Mahi, hosted on Kaggle (199,611 leaf images across 58 subdirectories), which the authors explicitly state is publicly available. No author analysis code, trained model checkpoint, or supplement with code/