A Novel Multi-Class Deep Learning Approach for Tomato Leaf Disease Detection System
Keywords:
Deep learning, Thresholding, Normalization, Edge Detection, Gaussian BlurAbstract
Tomato leaf infections pose a prevalent risk to sustained tomato cultivation, impacting numerous producers on a global scale. The timely identification, management, and resolution of tomato leaf specificity are of utmost importance in fostering optimal growth of tomato plants and guaranteeing an abundant supply of tomatoes to meet the increasing global demand and provide food security. The utilisation of computer-assisted technology for the identification and diagnosis of diseases presents in plant leaves is currently widespread. This study utilises about 10,000 tomato leaf photos sourced from the PlantVillage standard library to perform object localization. This paper presents a proposed Deep Learning approach that demonstrates effectiveness in autonomously segmenting and detecting diseases in tomato plant leaves. This work employed an image processing approches for the purpose of pre-processing and segmentation, alongside a multi-class convolutional neural network, in order to categorise ten distinct categories of diseases affecting tomato plant leaves. The process of segregating the contaminated area from the unaffected regions of the image was accomplished by the utilisation of thresholding, Gaussian blur, and canny edge detection techniques. Subsequently, the features were extracted by employing a Convolutional Neural Network model. The underlying model is evaluated by using the activation functions Relu and Leaky_Relu. It has been observed that the performance of presented architectures is superior when utilising the leaky_relu activation function contrasted to the relu activation function. Specifically, the training accuracy achieved with relu is 92.76%, whereas with leaky_relu it is 95.08%.
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