Scheduling is assigning shared resources over time to efficiently complete the tasks over a given period of time. The term is applied separately for tasks and resources correspondingly in task scheduling and resource allocation. Scheduling is a popular topic in operational management and computer science. Effective schedules ensure system efficiency, effective decision making, minimize resource wastage and cost, and enhance overall productivity. It is generally a tedious task to choose the most accurate resources in performing work items and schedules in both computing and business process execution. Especially in real-world dynamic systems where multiple agents involve in scheduling various dynamic tasks is a challenging issue. Reinforcement Learning is an emergent technology which has been able to solve the problem of the optimal task and resource scheduling dynamically. This review paper is about a research study that focused on Reinforcement Learning techniques that have been used for dynamic task scheduling. The paper addresses the results of the study by means of the state-of-theart on Reinforcement learning techniques used in dynamic task scheduling and a comparative review of those techniques.

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The impact of rice plant diseases has led to a 37% annual drop in rice production. It may happen basically due to the lack of knowledge in identifying and controlling rice plant diseases, but still there isn’t any proper application has been developed which is capable enough to identify these rice plant diseases accurately and control those diseases. In order to identify rice plant disease by an application itself, Convolutional Neural Networks (CNN) can be used. Many of researchers have used CNNs for plant disease identification because of their accuracy in image identification and classification. But, there’s still a handful researches have been conducted regarding the identification of rice plant diseases. This study provides a comprehensive understanding of current rice plant illnesses as well as the Deep Learning approaches used to detect such diseases. It also analyzes several kinds of techniques that have been employed in the literature by analyzing all of them with their benefits and drawbacks. It has discovered the most accurate ways in all levels of the image identification procedure as a consequence of this research, that will be valuable in recognizing rice plant illnesses.

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