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Analyzing various CNN architectures for the detection of plant diseases

  ABSTRACT   Diseases in potato plants cause tremendous damage to the yield. CNN constitutes a possible solution for the early detection of early and late blight diseases. This paper does a detailed comparison based on the performance and suitability of a new model and a Pre-Trainemodel     I cont ext of detecting an early blight or a late blight in Potato Crops. This investigation comprises a comparative analysis including their accuracy and loss. Through meticulous testing and training, this study aims to provide cognizance into the efficiency of these architectures the for automated identification of blight diseases in potatoes, important for crop disease management. The images after adequate preprocessing and augmentation (by using techniques such as Random Flip and Random Rotatiofede fed to the five pre-trained models-InceptionV3, ResNet152V2, MobileNetV2, DESNET12,1 and VGG16 neural network architectures and one custom CNN model over a total of 10 epochs; o...
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Revolutionizing Agriculture: Analyzing Trends in CNN for Plant Disease Detection

Revolutionizing Agriculture: Analyzing Trends in CNN for Plant Disease Detection Introduction: The agricultural sector, a cornerstone of the Indian economy, faces a constant battle against plant diseases that threaten crop health and yield. Traditionally, manual inspection has been the primary method for detecting these diseases, but advancements in technology, particularly Convolutional Neural Networks (CNNs), are revolutionizing this process. In this blog, we delve into the significance of CNNs in plant disease detection, exploring their advantages, methodologies, and the implications for agriculture. Advantages of CNN-based Detection: CNNs offer several advantages over traditional methods: 1. Early Detection: CNNs can identify diseases before visible symptoms appear, enabling timely interventions to prevent widespread damage. 2. Accuracy: With vast datasets, CNNs can achieve higher accuracy in disease identification compared to human inspectors. 3. Automation: CNNs automate the dete...
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