Abstract: Cotton plays a crucial role in the global economy and is a primary raw material for the textile sector. Despite its importance, cotton crops are prone to various diseases that can severely ...
Abstract: Timely and accurate identification of plant diseases is essential for sustainable agricultural practices and food security. This study presents a deep learning-based diagnostic framework ...
Abstract: Accurate early diagnosis of plant diseases must be ensured for proper agricultural output and minimizing losses economically. Hybrid optimization using deep learning is utilized by the ...
Abstract: Global food security is still threatened by crop diseases that cause reductions in so if n yield as well as excessive financial cost to the farmer. In practice, traditional field inspections ...
Abstract: The presence of plant diseases creates major difficulties for agricultural production that causes significant monetary damage while threatening food availability to populations. Machine ...
Abstract: Early detection of plant disease is useful in reducing its rapid spread; however similar visual appearances of different plant diseases make it a challenging problem. In the proposed ...
Abstract: Early detection of plant diseases is vital for enhancing agricultural output and ensuring global food security. This paper introduces a robust and scalable Plant Disease Detection System ...
Abstract: In a country like India, where agriculture provides for both nationwide consumption and merchandise exports, plant disease is one of the most significant factors that might impact crop ...
This project uses deep learning to automatically detect plant diseases from leaf images. The model leverages Convolutional Neural Networks (CNNs) and Transfer Learning (MobileNetV2) for accurate and ...
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