Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review.

In most developing countries, the contribution of agriculture to gross domestic product is significant. Plant disease is one of the major factors that adversely affect crop yield. Traditional plant disease detection techniques are time-consuming, biased, and ineffective. Potato is among the top consumed plants in the world, in general, and in developing countries, in particular. However, potato is affected by different kinds of diseases which minimize their yield and quantity. The advancement in AI and machine learning has paved the way for new methods of tackling plant disease detection. This study presents a comprehensive systematic literature review on the major diseases that harm potato crops. In this effort, computer vision-based techniques are employed to identify potato diseases, and types of machine learning algorithms used are surveyed. In this review, 39 primary studies that have provided useful information about the research questions are chosen. Accordingly, the most common potato diseases are found to be late blight, early blight, and bacterial wilt. Furthermore, the review discovered that deep learning algorithms were more frequently used to detect crop diseases than classical machine learning algorithms. Finally, the review categorized the state-of-the-art algorithms and identifies open research problems in the area.

A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques.

The rapid growth of cloud computing environment with many clients ranging from personal users to big corporate or business houses has become a challenge for cloud organizations to handle the massive volume of data and various resources in the cloud. Inefficient management of resources can degrade the performance of cloud computing. Therefore, resources must be evenly allocated to different stakeholders without compromising the organization's profit as well as users' satisfaction. A customer's request cannot be withheld indefinitely just because the fundamental resources are not free on the board. In this paper, a combined resource allocation security with efficient task scheduling in cloud computing using a hybrid machine learning (RATS-HM) technique is proposed to overcome those problems. The proposed RATS-HM techniques are given as follows: First, an improved cat swarm optimization algorithm-based short scheduler for task scheduling (ICS-TS) minimizes the make-span time and maximizes throughput. Second, a group optimization-based deep neural network (GO-DNN) for efficient resource allocation using different design constraints includes bandwidth and resource load. Third, a lightweight authentication scheme, i.e., NSUPREME is proposed for data encryption to provide security to data storage. Finally, the proposed RATS-HM technique is simulated with a different simulation setup, and the results are compared with state-of-art techniques to prove the effectiveness. The results regarding resource utilization, energy consumption, response time, etc., show that the proposed technique is superior to the existing one.