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Smart agriculture holds a transformative potential, driven by cutting-edge technologies, in revolutionizing the global food production sector and enhancing food safety measures. This is achieved by leveraging the capabilities of smart maps, artificial intelligence, and agricultural data analytics. Hence, farmers can benefit from it as it allows them to streamline resource utilization. This, in turn, leads to reduced inputs of fertilizers, labor, seeds, and water, while simultaneously amplifying crops. Therefore, this paper introduces a comprehensive system for advancing smart map agriculture, comprising five pivotal stages designed to yield accurate and meaningful results. Notable progress is achieved through meticulous data acquisition by employing the rice seedling and WeedNet datasets, coupled with feature extraction utilizing the MobileNet architecture. The classification stage is critical for distinguishing between rice and land in the first dataset and between crops and weeds in the second one. It further enhances the system’s efficacy. Additionally, segmentation using K-means clustering to detect rice or crop is employed. Notably, the current approach exhibits promising performance levels. It results in 99.7%, 100%, 99.4%, and 99.72% for accuracy, precision, recall, and F1_score, respectively, in the rice seedling dataset, as well as achieving 98.72%, 97.3%, 100%, and 98.63% in the WeedNet dataset. The primary contribution of this study lies in the integration of MobileNet and SVM architectures for feature extraction and classification in the agriculture field. It also presents the innovative use of K-means clustering for segmenting agricultural fields.

Speech recognition systems play an important role in human–machine interactions. Many systems exist for Arabic speech, however, there are limited systems for dialectal Arabic speech. The Arabic language comprises many properties, some of which are ideal for building automatic speech recognition systems such as syntax and phonology, while other properties are unsuitable for developing speech systems. Importantly, most data are in non-diacritized form, vary in dialect, and contain morphological complexity. Moreover, the Arabic dialects lack a standard structure. In this paper, we highlighted the works and frameworks that have been developed in the last fourteen years of dialectal Arabic speech recognition systems. The paper also presented an analysis and evaluation for several studies using different approaches and techniques. The main goal of this paper is to compare and discuss different studies in dialectal Arabic speech systems including several criteria such as techniques, datasets, evaluation metrics, and dialect types. The study also includes a description of some techniques used in all steps of the dialectal Arabic speech system such as hidden Markov models (HMM), convolutional neural network (CNN), and deep neural network (DNN). In addition, we introduced the challenges and problems of dialectal Arabic speech recognition systems. Overall, more studies are required to obtain a more accurate speech system for dialectal Arabic.