Lettuce Plant Disease Recognition Using Android-Based CNN Algorithm Method

Abstract

Introduction: Disease detection in lettuce (Lactuca sativa L.) is crucial to enhance crop yields and prevent losses caused by bacterial, fungal, and weed-related infections. This study aimed to develop an Android-based lettuce disease detection application using a Convolutional Neural Network (CNN) algorithm to assist farmers in identifying plant diseases in real time. Method: The research used a dataset of 2,320 lettuce leaf images obtained from Kaggle, categorized as healthy, bacterial, fungal, and shepherd’s purse weed. The dataset was preprocessed through labeling, normalization, and augmentation to improve model robustness. The CNN architecture comprised four convolution layers followed by max-pooling, dense, and softmax output layers. The model was trained using TensorFlow and deployed through TensorFlow Lite for mobile implementation. Results: The CNN model achieved 93,67 % training accuracy and 93,99 % validation accuracy, demonstrating good generalization without overfitting. The evaluation using confusion matrix and classification reports showed high performance, particularly in identifying healthy and shepherd’s purse weed categories with F1-scores of 0.94 and 0.99, respectively. The Android application successfully detected diseases in real time and provided users with diagnostic results, historical data, and treatment suggestions. Conclusions: The developed CNN-based Android application proved effective for automatic lettuce disease detection with high accuracy and practical usability for farmers. Future studies could enhance performance through more advanced CNN architectures such as VGG16 or ResNet50 and the use of more detailed datasets for improved disease classification.

Plant phenotyping relevance

CNNによるレタス葉画像からの病害状態推定とAndroidアプリ実装が研究の中心であり、モデル性能も検証しているため、植物フェノタイピング手法に該当する。

Code and data availability

The paper uses a Kaggle lettuce leaf image dataset and a TensorFlow/TFLite CNN, but provides no authors' public URL, deposit, or availability statement for the dataset, code, or trained model. The Kaggle dataset is mentioned without a link or identifier, and no supplementary assets are described.

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