Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism.

Abstract

Tomatoes are considered one of the most valuable vegetables around the world due to their usage and minimal harvesting period. However, effective harvesting still remains a major issue because tomatoes are easily susceptible to weather conditions and other types of attacks. Thus, numerous research studies have been introduced based on deep learning models for the efficient classification of tomato leaf disease. However, the usage of a single architecture does not provide the best results due to the limited computational ability and classification complexity. Thus, this research used Transductive Long Short-Term Memory (T-LSTM) with an attention mechanism. The attention mechanism introduced in T-LSTM has the ability to focus on various parts of the image sequence. Transductive learning exploits the specific characteristics of the training instances to make accurate predictions. This can involve leveraging the relationships and patterns observed within the dataset. The T-LSTM is based on the transductive learning approach and the scaled dot product attention evaluates the weights of each step based on the hidden state and image patches which helps in effective classification. The data was gathered from the PlantVillage dataset and the pre-processing was conducted based on image resizing, color enhancement, and data augmentation. These outputs were then processed in the segmentation stage where the U-Net architecture was applied. After segmentation, VGG-16 architecture was used for feature extraction and the classification was done through the proposed T-LSTM with an attention mechanism. The experimental outcome shows that the proposed classifier achieved an accuracy of 99.98% which is comparably better than existing convolutional neural network models with transfer learning and IBSA-NET.

Plant phenotyping relevance

トマト葉の病害状態を画像から推定する分類・セグメンテーション手法を提案し、前処理、U-Net、特徴抽出、T-LSTM分類、比較評価までが中心であるため、植物病害フェノタイピング手法として含める。

Code and data availability

The paper uses three public Kaggle tomato leaf image datasets: the PlantVillage tomato leaf dataset as the primary training/evaluation data, and two additional Kaggle image datasets for the independent real-time analysis. No author analysis code or trained model checkpoint is reported as publicly available.