标题： 使用深度学习检测多种作物中的多种疾病 显示英文标题

标题： Detecting Multiple Diseases in Multiple Crops Using Deep Learning

摘要： 印度作为一个以农业为主的经济体，在农业方面面临重大挑战，包括由病害、害虫和环境压力引起的大量作物损失。 在不同作物上早期检测和准确识别病害对于提高产量和确保粮食安全至关重要。 本文提出了一种基于深度学习的解决方案，用于检测多种作物中的多种病害，旨在覆盖印度多样化的农业景观。 我们首先创建了一个统一的数据集，涵盖了来自各种可用存储库的17种不同作物和34种不同病害的图像。 所提出的深度学习模型在此数据集上进行训练，并在准确性和所覆盖的作物及病害数量方面优于当前最先进的技术。 我们实现了显著的检测准确率，即对于我们的统一数据集达到99%，与仅处理14种作物和26种不同病害的最先进方法相比，提高了7%。 通过提高可检测的作物种类和病害类型数量，所提出的解决方案旨在为印度农民提供更好的产品。 显示英文摘要

摘要： India, as a predominantly agrarian economy, faces significant challenges in agriculture, including substantial crop losses caused by diseases, pests, and environmental stress. Early detection and accurate identification of diseases across different crops are critical for improving yield and ensuring food security. This paper proposes a deep learning based solution for detecting multiple diseases in multiple crops, aimed to cover India's diverse agricultural landscape. We first create a unified dataset encompassing images of 17 different crops and 34 different diseases from various available repositories. Proposed deep learning model is trained on this dataset and outperforms the state-of-the-art in terms of accuracy and the number of crops, diseases covered. We achieve a significant detection accuracy, i.e., 99 percent for our unified dataset which is 7 percent more when compared to state-of-the-art handling 14 crops and 26 different diseases only. By improving the number of crops and types of diseases that can be detected, proposed solution aims to provide a better product for Indian farmers.