Monitoring Tomato Leaf Disease through Convolutional Neural Networks

Agriculture plays an essential role in Mexico's economy. The agricultural sector has a 2.5% share of Mexico's gross domestic product. Specifically, tomatoes have become the country's most exported agricultural product. That is why there is an increasing need to improve crop yields. One of the elements that can considerably affect crop productivity is diseases caused by agents such as bacteria, fungi, and viruses. However, the process of disease identification can be costly and, in many cases, time-consuming. Deep learning techniques have begun to be applied in the process of plant disease identification with promising results. In this paper, we propose a model based on convolutional neural networks to identify and classify tomato leaf diseases using a public dataset and complementing it with other photographs taken in the fields of the country. To avoid overfitting, generative adversarial networks were used to generate samples with the same characteristics as the training data. The results show that the proposed model achieves a high performance in the process of detection and classification of diseases in tomato leaves: the accuracy achieved is greater than 99% in both the training dataset and the test dataset.

Automated summary

Agriculture is important for Mexico's economy, with the agricultural sector contributing 2.5% to the country's GDP. Tomatoes are the most exported agricultural product, highlighting the need for improved crop yields. This study proposes a model based on convolutional neural networks for identifying and classifying tomato leaf diseases, using a combination of a public dataset and additional field photographs. The results demonstrate the model's high performance, achieving over 99% accuracy in both the training and test datasets.