Detection of leaf disease in tomato plants using a lightweight parallel deep convolutional neural network

Abstract

Plant diseases and poisonous insects are major threats to agriculture. As a result, detecting and diagnosing these illnesses as soon as feasible is critical. The ongoing development of major deep learning techniques has substantially aided in the diagnosis of plant leaf diseases, providing a potent instrument with incredibly exact results. Deep learning algorithms, on the other hand, are dependent on the quality and quantity of labelled data used for training. The lightweight parallel deep convolutional neural network is described in this study for detecting plant leaf disease. In addition, the Generative Adversarial Neural Network is introduced for creating synthetic data in order to overcome the data scarcity problem caused by uneven dataset size. The experimental results for two-class, six-class and ten-class disease identification of tomato plant samples from the Plant Village dataset are provided. The effectiveness of the proposed model is assessed using numerous performance measures, including accuracy, recall, precision and F1-score, and compared to known state-of-the-art approaches for tomato plant leaf disease detection. The proposed system provides better accuracy (99.14%, 99.05%, 98.11% accuracy for the 2-class, 6-class and 10-class) for tomato leaf disease detection compared with traditional existing approaches.

Keywords:

• Deep learning
• precision agriculture
• plant leaf disease detection
• data augmentation

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Not Applicable