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Wheat is a major nutritional cereal crop that has economic and strategic value worldwide. The sustainability of this extraordinary crop is facing critical challenges globally, particularly leaf rust disease, which causes endless problems for wheat farmers and countries and negatively affects humanity’s food security. Developing effective marker-assisted selection programs for leaf rust resistance in wheat mainly depends on the availability of deep mining of resistance genes within the germplasm collections. This is the first study that evaluated the leaf rust resistance of 50 Egyptian wheat varieties at the adult plant stage for two successive seasons and identified the absence/presence of 28 leaf rust resistance (Lr) genes within the studied wheat collection. The field evaluation results indicated that most of these varieties demonstrated high to moderate leaf rust resistance levels except Gemmeiza 1, Gemmeiza 9, Giza162, Giza 163, Giza 164, Giza 165, Sids 1, Sids 2, Sids 3, Sakha 62, Sakha 69, Sohag 3 and Bany Swif 4, which showed fast rusting behavior. On the other hand, out of these 28 Lr genes tested against the wheat collection, 21 Lr genes were successfully identified. Out of 15 Lr genes reported conferring the adult plant resistant or slow rusting behavior in wheat, only five genes (Lr13, Lr22a, Lr34, Lr37, and Lr67) were detected within the Egyptian collection. Remarkedly, the genes Lr13, Lr19, Lr20, Lr22a, Lr28, Lr29, Lr32, Lr34, Lr36, Lr47, and Lr60, were found to be the most predominant Lr genes across the 50 Egyptian wheat varieties. The molecular phylogeny results also inferred the same classification of field evaluation, through grouping genotypes characterized by high to moderate leaf rust resistance in one cluster while being highly susceptible in a separate cluster, with few exceptions. Full article

This paper proposes two algorithms for adaptive driving in urban environments: the first uses vision deep learning, which is named the sparse spatial convolutional neural network (SSCNN); and the second uses a sensor integration algorithm, named the sensor-weighted integration field (SWIF). These algorithms utilize three kinds of sensors, namely vision, Light Detection and Range (LiDAR), and GPS sensors, and decide critical motions for autonomous vehicle, such as steering angles and vehicle speed. SSCNN, which is used for lane recognition, has 2.7 times faster processing speed than the existing spatial CNN method. Additionally, the dataset for SSCNN was constructed by considering both normal and abnormal driving in 7 classes. Thus, lanes can be recognized by extending lanes for special characteristics in urban settings, in which the lanes can be obscured or erased, or the vehicle can drive in any direction. SWIF generates a two-dimensional matrix, in which elements are weighted by integrating both the object data from LiDAR and waypoints from GPS based on detected lanes. These weights are the integers, indicating the degree of safety. Based on the field formed by SWIF, the safe trajectories for two vehicles’ motions, steering angles, and vehicle speed are generated by applying the cost field. Additionally, to flexibly follow the desired steering angle and vehicle speed, the Proportional-Integral-Differential (PID) control is moderated by an integral anti-windup scheme. Consequently, as the dataset considers characteristics of the urban environment, SSCNN is able to be adopted for lane recognition on urban roads. The SWIF algorithm is also useful for flexible driving owing to the high efficiency of its sensor integration, including having a resolution of 2 cm per pixel and speed of 24 fps. Thus, a vehicle can be successfully maneuvered with minimized steering angle change, without lane or route departure, and without obstacle collision in the presence of diverse disturbances in urban road conditions. Full article