Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

稻米是全世界必需的营养素。稻米占全世界人均能量的21％和人均蛋白质的15％。亚洲占世界人口的60％，约占世界大米产量的92％，占世界大米利用量的90％。随着人口的增加，对稻米的需求也在增加。因此，需要通过引进新技术来提高农业生产率。深度学习和物联网是各个领域研究的热门话题。本文提出了一种包含深度学习和物联网的设置，用于远程监测稻田。考虑使用vgg16预训练网络来识别稻叶病和估算氮素状况。在这里，执行两种策略来识别图像：传递学习和深度特征提取。深度特征提取方法与支持向量机（SVM）相结合以对图像进行分类。vgg16的转移学习方法可用于识别四种类型的叶病并预测氮的状况，其准确度为79.86％和84.88％。再次，Vgg16和SVM结果的深层特征可用于识别四种类型的叶病和预测氮素状况，其准确度分别为97.31％和99.02％。此外，提出了一个基于物联网和深度学习的稻田远程监测框架。建议的原型的优越性在于它可以像最新技术一样控制温度和湿度，并且可以监视其他两个方面，例如检测氮的状况和疾病。vgg16的转移学习方法可用于识别四种类型的叶病并预测氮的状况，其准确度为79.86％和84.88％。再次，Vgg16和SVM结果的深层特征可用于识别四种类型的叶病和预测氮素状况，其准确度分别达到97.31％和99.02％。此外，提出了一个基于物联网和深度学习的稻田远程监测框架。建议的原型的优越性在于它可以像最新技术一样控制温度和湿度，并且可以监视其他两个方面，例如检测氮的状况和疾病。vgg16的转移学习方法可用于识别四种类型的叶病并预测氮的状况，其准确度为79.86％和84.88％。再次，Vgg16和SVM结果的深层特征可用于识别四种类型的叶病和预测氮素状况，其准确度分别为97.31％和99.02％。此外，提出了一个基于物联网和深度学习的稻田远程监测框架。建议的原型的优越性在于它可以像最新技术一样控制温度和湿度，并且可以监视其他两个方面，例如检测氮的状况和疾病。Vgg16和SVM结果的深层特征可用于识别四种类型的叶病和预测氮素状况，其准确率分别为97.31％和99.02％。此外，提出了一个基于物联网和深度学习的稻田远程监测框架。建议的原型的优越性在于它可以像最新技术一样控制温度和湿度，并且可以监视其他两个方面，例如检测氮的状况和疾病。Vgg16和SVM结果的深层特征可用于识别四种类型的叶病和预测氮素状况，其准确率分别为97.31％和99.02％。此外，提出了一个基于物联网和深度学习的稻田远程监测框架。建议的原型的优越性在于它可以像最新技术一样控制温度和湿度，并且可以监视其他两个方面，例如检测氮的状况和疾病。