Development and Evaluation of a Distinctive Cloud-Based Artificial Intelligence System using Deep Learning Techniques (AISDLT) for Accurate Detection of Tomato Plant Leaf Diseases
Keywords:
Tomato Plant, Leaf Disease Detection, ResNet, VGG16, CNN-based models, Transfer learning, Data Augmentation (DA), Genetic Algorithm, Image SegmentationAbstract
Fighting tomato infections is crucial for the agriculture industry since they can cause large drops in crop output. Therefore, it is crucial to recognize and categorize tomato leaf diseases as soon as possible to minimize the loss. Nevertheless, this is a tedious and drawn-out procedure. To tackle the above-listed problems, an accurate automated method for timely identification and classification is required. To properly diagnose and notify the individual about this sickness, the current approaches have created machine learning and deep learning algorithms that identify the illness from the foliage of tomatoes. Early and accurate detection of tomato plant leaf diseases is crucial for minimizing yield losses and ensuring food security. Traditional methods often rely on visual inspection by trained personnel, which can be time-consuming, subjective, and prone to errors. This paper proposes a distinctive cloud-based AI system powered by deep learning techniques for automated and accurate detection of tomato plant leaf diseases. The system leverages high-resolution images captured from smartphones or dedicated sensors in the field. A pre-trained convolutional neural network (CNN) model is fine-tuned on a disease-specific dataset to achieve high classification accuracy. We used Transfer learning for pre-trained models like ResNet or VGG16 can be fine-tuned with labelled datasets of diseased leaves, significantly reducing training time and improving accuracy. Data Augmentation (DA) is a techniques like rotating, flipping, and scaling images can significantly increase the size and diversity of your training data, leading to better generalization and robust performance. The cloud infrastructure facilitates efficient data storage, model training, and real-time disease detection, making the system readily accessible to farmers even with limited resources. The proposed architecture of the AI system, the chosen deep learning technique, the employed optimization algorithm (genetic algorithm), and the achieved detection accuracy.
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