Abstract
In the 21st-century crop complaint is a serious concern for food security. In this period, utmost of the husbandry support centers and numerous growers use different technologies to ameliorate productivity in farming, but still concerned about plant safety and fast detection of plant leaf diseases which remains difficult in different parts of the regions. Rice plants are frequently infected with diseases that can result in social and financial damage. Many rice crop diseases manifest themselves first on the leaves of the plants. As we say, automated rice plant disease diagnosis is an important aspect of food security, yield loss estimate, and disease management. As a result, computer vision and image processing are utilized to identify infected leave. With the proliferation of digital cameras and ongoing advancements in the computer vision area, automated disease detection techniques are in great demand in precision agriculture, high-yield agriculture, smart greenhouses, and other fields. This study uses an open dataset with 4 types of leaf infections, namely brown spot, blast, bacterial blight, and tungro. In this research, automatically identify plant leaf infection and classify whether the leaf is healthy or diseased, instead of the traditional overlong manual disease diagnostic method, deep CNN models may obtain the best accuracy. We have compared our suggested model (custom-CNN) against pre-trained deep CNN models i.e. VGG19, DensNet121, InceptionV3, as well as ResNet152 deep learning models, and with a learning rate of 0.001, the custom-CNN model obtained greater accuracy of 97.47%. This paper is willing to support and help the farmer Community of the world.
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Mohapatra, S., Marandi, C., Sahoo, A., Mohanty, S., Tudu, K. (2022). Rice Leaf Disease Detection and Classification Using a Deep Neural Network. In: Panda, S.K., Rout, R.R., Sadam, R.C., Rayanoothala, B.V.S., Li, KC., Buyya, R. (eds) Computing, Communication and Learning. CoCoLe 2022. Communications in Computer and Information Science, vol 1729. Springer, Cham. https://doi.org/10.1007/978-3-031-21750-0_20
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