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The pathogens such as fungi and bacteria can lead to rice diseases that can drastically impair crop production. Because the illness is difficult to control on a broad scale, crop field monitoring is one of the most effective methods of control. It allows for early detection of the disease and the implementation of preventative measures. Disease severity estimation based on digital picture analysis, where the pictures are obtained from the rice field using mobile devices, is one of the most effective control strategies. This paper offers a method for quantifying the severity of three rice crop diseases (brown spot, blast, and bacterial blight) that can determine the stage of plant disease. A total of 1200 images of rice illnesses and healthy images make up the input dataset. With the help of agricultural experts, the diseased zone was labeled according to the disease type using the Make Sense tool. More than 75% of the images in the dataset correspond to one disease label, healthy plants represent more than 15%, and multiple diseases represent 5% of the images labeled. This paper proposes a novel artificial intelligence rice grade model that uses an optimized faster-region-based convolutional neural network (FRCNN) approach to calculate the area of leaf instances and the infected regions. EfficientNet-B0 architecture was used as a backbone as the network shows the best accuracy (96.43%). The performance was compared with the CNN architectures: VGG16, ResNet101, and MobileNet. The model evaluation parameters used to measure the accuracy are positive predictive value, sensitivity, and intersection over union. This severity estimation method can be further deployed as a tool that allows farmers to obtain perfect predictions of the disease severity level based on lesions in the field conditions and produce crops more organically.

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Data management and sharing is the challenge being faced by all the IT majors today. Adds over it, is the challenge faced by the cloud service providers in terms of multi-tenancy of data and its efficient retrieval. It becomes more complex in a heterogeneous computing environment to provide cloud services. A simple, robust, query efficient, scalable and space saving multi-tenant database architecture is proposed along with an ad hoc cloud architecture where organizations can collaborate to create a cloud, that doesnt harm their existence or profitability. An ad hoc cloud fits very well to the scenario where one wants to venture into remote areas for providing education services using a cloud. The results of the proposed multi-tenant database show 20% to 230% improvement for insertion, deletion and updation-queries. The response of the proposed approach is stable as compared to other system which degrades in terms of response time by 384% for increased number of attributes up to 50. The proposed approach is also space efficient by almost 86%. Dynamically changing cloud configurations requires adaptable database and mechanism to persist and manage data and exploit heterogeneous resources. The proposed ad hoc cloud handles heterogeneity of the involved nodes and deals with node specific granularity while decomposing workloads for efficient utilization of resources.