Improving Tomato Leaf Disease Detection with DenseNet-121 Architecture
Keywords:
DensNet-121, Tomato Leaf Disease Detection, Gradient Vanishing, Deep Learning CNNAbstract
This study introduces the DenseNet-121 architecture, a unique method for detecting diseases in tomato leaves. The goal is to use novelty detection methods to spot diseases that have not been seen before, while also fixing the gradient vanishing problem plaguing deep learning models. The proposed method is meant to aid in the early diagnosis and mitigation of potential crop losses by providing accurate and robust disease detection for tomato plants. DenseNet-121 is used as a solution to this problem. When it comes to deep learning models, DenseNet-121 is at the cutting edge because of its innovative use of dense skip connections across layers. The gradient vanishing problem can be alleviated thanks to the direct flow of gradients made possible by these links. The proposed method incorporates DenseNet-121 to boost the disease detection model's performance and convergence. The experimental evidence supports the efficiency of the proposed approach. Evaluation measures including accuracy, precision, recall, and F1-score are used to compare the disease detection system's performance to that of established methodologies or baselines. The system's adaptability and capacity to correctly identify new diseases are also evaluated in detail, showing that it can go beyond what was previously known. This study introduces the DenseNet-121 architecture, a novel method for disease detection in tomato leaves, and shows how it may be used to solve the gradient vanishing problem. The suggested methodology incorporates novelty detection techniques to improve diagnostic accuracy for rarely observed diseases. The outcomes demonstrate the system's efficacy and resilience in detecting and preventing losses in tomato crops at an early stage. Extensions and upgrades are discussed as well.
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