Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization - V., Pandiyaraju; Kumar, A. M. Senthil; Praveen, Joe I. R.; Venkatraman, Shravan; Kumar, S. Pavan; Aravintakshan, S. A.; Abeshek, A.; Kannan, A. (Frontiers in Plant Science, 2024)
Tomato is one of the most popular and important food crops used in all the countries of the world. The quality and quantity of yield by the tomato plants are affected due to the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants for improving the overall crop yield and to support the farmers. In the past, many research works were carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifiers with the support from swarm intelligence-based optimization techniques are able to enhance the classification accuracy leading to the accurate detection of leaf diseases more effectively. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and enhanced weighted gradient optimizer that is integrated into a finetuned VGG16 and NASNet mobile training methods for providing improved learning and classification accuracy. The dataset used for the research comprises of 10,000 tomato leaf images, categorized into nine classes for training, validating, and reserved 1000 images for testing the model. The results have been analysed thoroughly and benchmarked with existingperformance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve and F1-Score by providing 98.7%, 4%, 97.9%, 98.6%, 99.97% and 98.7% respectively.