Dec 1, 2023 · Proposed a deep learning-based model for crop disease detection. Provides a higher accuracy rate of 98.98% using ResNet-50 for disease detection. Ensure farmers save …

To assist the identification and detection of different crops that characterize the agricultural missions, we introduce a novel domain-specific dataset named CropDeep, which consists of …

Aug 23, 2024 · In order to enhance the precision, we have suggested implementing a deep learning technique, specifically a convolutional neural network, to identify the crops. Achieving …

deep neural network is the prominent tool in agricultural industry for providing support to farmers in monitoring crop yield based on the weather conditions. In this paper, the recurrent neural …

By harnessing convolutional neural networks (CNNs) and leveraging transfer learning techniques, our method showcases exceptional accuracy in identifying diseased crops across varying …

Hence, computer vision employed with deep learning provides the way to solve this problem. This paper proposes a deep learning-based model which is trained using public dataset containing …

Aug 28, 2023 · In the experiment, we examined CNN, VGG-16, VGG-19 and ResNet-50 models on plant-village 10000 image dataset to detect crop infection and got the accuracy rate of …

Feb 10, 2025 · Convolutional Neural Networks (CNNs) have become the most widely used deep learning approach for crop disease detection. These models analyze leaf images, extract …

Detecting crops, particularly rice fields, can be challenging due to the presence of water content. Waterlogged rice fields can lead to similar spectral signatures as other water bodies, making it …

Feb 6, 2025 · In this paper, we propose CropDP-KG, a knowledge graph for crop diseases and pests in China, which leverages natural language processing techniques to analyze data from …