In mobile communication, the most exploratory technology of fifth generation is massive multiple input multiple output (MIMO). The minimum mean square error and zero forcing based linear detectors are used in multiuser detection for MIMO single‐carrier frequency division multiple access (SC‐FDMA). When the received signal is detected and regularization sequence is joined in the equalization of spectral null amplification, these schemes experience an error performance and the signal detection assesses an inversion of a matrix computation that grows into complexity. Ordered successive interference cancelation (OSIC) detection is considered for MIMO SC‐FDMA, which uses a posteriori information to eradicate these problems in a realistic environment. To cancel the interference, sorting is preferred based on signal‐to‐noise ratio and log‐likelihood ratio. The distinctiveness of the methodology is to predict the symbol with the lowest error probability. The proposed work is compared with the existing methods, and simulation results prove that the defined algorithm outperforms conventional detection methods and accomplishes better performance with lower complication.

Rice is the primary food for almost half of the world’s population, especially for the people of Asian countries. There is a demand to improve the quality and increase the quantity of rice production to meet the food requirements of the increasing population. Bulk cultivation and quality production of crops need appropriate technology assistance over manual traditional methods. In this work, six popular Deep-CNN architectures, namely AlexNet, VGG-19, VGG-16, InceptionV3, MobileNet, and ResNet-50, are exploited to identify the diseases in paddy plants since they outperform most of the image classification applications. These CNN models are trained and tested with Plant Village dataset for classifying the paddy plant images into one of the four classes namely, Healthy, Brown Spot, Hispa, or Leaf Blast, based on the disease condition. The performance of the chosen architectures is compared with different hyper parameter settings. AlexNet outperformed other convolutional neural networks (CNNs) in this multiclass classification task, achieving an accuracy of 89.4% at the expense of a substantial number of network parameters, indicating the large model size of AlexNet. For developing mobile applications, the ResNet-50 architecture was adopted over other CNNs, since it has a comparatively smaller number of network parameters and a comparable accuracy of 86.1%. A fine-tuned ResNet-50 architecture supported mobile app, “Generic Paddy Plant Disease Detector (GP2D2)” has been developed for the identification of most commonly occurring diseases in paddy plants. This tool will be more helpful for the new generation of farmers in bulk cultivation and increasing the productivity of paddy. This work will give insight into the performance of CNN architectures in rice plant disease detection task and can be extended to other plants too.