Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamilnadu, India.
Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana, India.
Environ Res. 2021 Jul;198:111275. doi: 10.1016/j.envres.2021.111275. Epub 2021 May 11.
Rice (Oryza sativa) is a principal cereal crop in the world. It is consumed by greater than half of the world's population as a staple food for energy source. The yield production quantity and quality of the rice grain is affecting by abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, virus, etc. For disease management, farmers spending lot of time and resources and they detect the diseases through their penniless naked eye approach which leads to unhealthy farming. The advancement of technical support in agriculture greatly assists for automatic identification of infectious organisms in the rice plants leaves. The convolutional neural network algorithm (CNN) is one of the algorithms in deep learning has been triumphantly invoked for solving computer vision problems like image classification, object segmentation, image analysis, etc. In our work, InceptionResNetV2 is a type of CNN model utilized with transfer learning approach for recognizing diseases in rice leaf images. The parameters of the proposed model is optimized for the classification task and obtained a good accuracy of 95.67%.
水稻（Oryza sativa）是世界上主要的粮食作物之一。它被世界上一半以上的人口作为主食，是能量的来源。水稻的产量、质量受到降水、土壤肥力、温度、病虫害等非生物和生物因素的影响。为了进行疾病管理，农民花费大量的时间和资源，他们通过肉眼观察来检测疾病，这导致了不健康的农业。农业技术的进步极大地帮助了水稻叶片中传染性生物体的自动识别。卷积神经网络算法（CNN）是深度学习算法之一，已经成功地应用于解决计算机视觉问题，如图像分类、目标分割、图像分析等。在我们的工作中，InceptionResNetV2 是一种 CNN 模型，我们利用迁移学习方法来识别水稻叶片中的疾病。该模型的参数经过优化，用于分类任务，获得了 95.67%的良好准确率。
