An image based prediction system for early blight disease in tomato plants using deep learning algorithm

Abstract

The global economic system directly depends on the agricultural and farming industry. However, plant diseases are a peril to crops that deteriorates the quality as well as quantity of agricultural produce which pose huge loss to the economy. Therefore, early detection of diseases is crucial to prevent the wrecking of crops. The main aim of this study is to detect the early blight disease in tomato plants beforehand using a prominent deep learning approach, i.e., Convolutional Neural Network (CNN). The current study uses an image-based Tomato Early Blight Disease (TEBD) dataset to develop a disease prediction model. Various image processing techniques, i.e., Background Removal, Augmentation, Resizing, Noise Removal, and Segmentation were applied to obtain a refined dataset. Further, the modified TEBD dataset was trained using the CNN approach to develop an image-based TEBD prediction model. The impact of hyperparameters such as epochs and mini-batch size were also analyzed for different train-test ratios. Afterwards, the performance of the proposed model was precisely evaluated using various performance metrics, i.e., Accuracy, F1-Score, Test loss, Precision, and Recall. The mean value was calculated of different performance metrics for all nine splitting ratios. The best mean accuracy achieved was 98.10% having batch-size 64 and 15 epochs. This model can be used by farmers to reduce the workload and thus will become feasible for them in early treatment and curing of disease. In this way, the proposed approach will prevent the plants from getting severely affected in the early stage.

Subject Classification:

• 68
• 68Txx
• 68T45

Keywords:

• Tomato
• Plant disease
• Early blight
• Convolutional neural network
• Image pre-processing
• Segmentation