No abstract

Cereals are an important and major source of the human diet. They constitute more than two-thirds of the world’s food source and cover more than 56% of the world’s cultivatable land. These important sources of food are affected by a variety of damaging diseases, causing significant loss in annual production. In this regard, detection of diseases at an early stage and quantification of the severity has acquired the urgent attention of researchers worldwide. One emerging and popular approach for this task is the utilization of machine learning techniques. In this work, we have identified the most common and damaging diseases affecting cereal crop production, and we also reviewed 45 works performed on the detection and classification of various diseases that occur on six cereal crops within the past five years. In addition, we identified and summarised numerous publicly available datasets for each cereal crop, which the lack thereof we identified as the main challenges faced for researching the application of machine learning in cereal crop detection. In this survey, we identified deep convolutional neural networks trained on hyperspectral data as the most effective approach for early detection of diseases and transfer learning as the most commonly used and yielding the best result training method

Ethiopia's coffee export accounts for about 34% of all exports for the budget year 2019/2020. Making it the 10th-largest coffee exporter in the world. Coffee diseases cause around 30% loss in production annually. In this paper, we propose an approach for the detection of four classes of coffee leaf diseases, Rust, Miner, Cercospora, and Phoma by using a fast Hue, Saturation, and Value (HSV) color space segmentation and a Mo-bileNetV2 architecture trained by transfer learning.The proposed HSV color segmentation algorithm constitutes of separating the leaf from the background and separating infected spots on the leaf by automatically finding the best threshold value for the Saturation (S) channel of the HSV color space. The algorithm was compared to the YCgCr and k-means algorithms, in terms of Mean Intersection Over Union and F1-Score.The proposed HSV segmentation algorithm outperformed these methods and achieved an MIoU score of 72.13% and an F1 score of 82.54%. The proposed algorithm also outperforms these methods in terms of execution time, taking on average 0.02 s per image for the segmentation of diseased spots from healthy leaf spots. Our MobileNetV2 classifier achieved a 96% average classification accuracy and 96% average precision. The segmentation accuracy and faster execution

The integration of IoT with the cloud infrastructure is essential for designing smart applications. However, such integration may lead to security issues. Authentication and session key establishment is an essential security requirement for secure communication between IoT devices and cloud servers. For evaluating authentication key agreement schemes, the extended Canetti–Krawczyk (eCK) adversary model is regarded to be a more strict and relevant adversary model. Many schemes for authenticated key exchange between IoT devices and cloud servers have been proposed in the literature but have been assessed under Dolev and Yoa (DY) adversary model. Recently, Rostampour et al. introduced an ECC-based approach for enabling authentication between IoT devices and cloud servers that is secure and robust to various attacks under the Dolev and Yoa adversary model. In this paper, a detailed review and the automated security verification of the Rostampour et al. scheme are carried out under the eCK adversary model using Scyther-Compromise. The validation indicates that the scheme is not secure and is susceptible to various attacks under the eCK adversary model. To overcome the limitation of the Rostampour et al. scheme, a design of an ECC-based scheme for authentication between IoT devices and cloud servers under the eCK adversary model is proposed. The Scyther verification indicates that the scheme is safe under the eCK adversary model. The soundness of the correctness of the proposed scheme has been analyzed using BAN logic. Comparative analysis indicates that the scheme is resilient under the eCK adversary model with an energy overhead of 278.16 mJ for a resource constraint IoT device and a communication overhead of 1,408 bits.

Fog computing is one of the prominent technology that bridges the gap between IoT nodes and cloud servers. For increasing the efficiency at the fog level, a fog federation can be employed. Fog federation at the fog level can be controlled by the fog coordinator. However, the information exchange between the fog coordinator and IoT nodes needs to be secured. Recently, a lightweight secure key exchange (LKSE) protocol for secure key exchange for fog federation was proposed. In this paper, the cryptanalysis of the LKSE is carried out. The cryptanalysis indicates that LKSE is vulnerable to spoofing and man in the middle attacks. To overcome the limitation of the LKSE, a design of an ECC-based secure key exchange protocol for IoT devices and fog coordinators is proposed. The security strength of the designed method has been evaluated using BAN logic and the random oracle model. Simulations on AVISPA have been performed for automatic security verification of the proposed method. A detailed security and functional comparison of the proposed scheme with LKSE have also been carried out.