A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> I. INTRODUCTION <s> The rhizosphere is the soil-plant root interphase and in practice consists of the soil adhering to the root besides the loose soil surrounding it. Plant growth-promoting rhizobacteria (PGPR) are potential agents for the biological control of plant pathogens. A biocontrol strain should be able to protect the host plant from pathogens and fulfill the requirement for strong colonization. Numerous compounds that are toxic to pathogens, such as HCN, phenazines, pyrrolnitrin, and pyoluteorin as well as, other enzymes, antibiotics, metabolites and phytohormones are the means by which PGPR act, just as quorum sensing and chemotaxis which are vital for rhizosphere competence and colonization. The presence of root exudates has a pronounced effect on the rhizosphere where they serve as an energy source, promoting growth and influencing the root system for the rhizobacteria. In certain instances they have products that inhibit the growth of soil-borne pathogens to the advantage of the plant root. A major source of concern is reproducibility in the field due to the complex interaction between the plant (plant species), microbe and the environment (soil fertility and moisture, day length, light intensity, length of growing season, and temperature). This review listed most of the documented PGPR genera and discussed their exploitation. <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> I. INTRODUCTION <s> With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper, different image segmentation techniques applied on optical remote sensing images are reviewed. The selection of papers include sources from image processing journals, conferences, books, dissertations and thesis out of more than 3000 journals, books and online research databases available at UNB. The conceptual details of the techniques are explained and mathematical details are avoided for simplicity. Both broad and detailed categorisations of reviewed segmentation techniques are provided. The state of art research on each category is provided with emphasis on developed technologies and image properties used by them. The categories defined are not always mutually independent. Hence, their interrelationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application <s> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> I. INTRODUCTION <s> Microorganisms play very important roles in people’s daily life. To discover the information of them is a fundamental work in microbiological studies, which can assist microbiologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. To this end, effective Content-based Microorganism Image Analysis (CBMIA) approaches using Artificial Neural Networks (ANNs) are introduced to microbiological fields from the 1990s. In order to clarify the development history and find the developing trend of ANNs in the CBMIA field, we briefly survey around 60 related works in this paper, including classical ANNs, deep ANNs and methodology analysis. <s> BIB003
There are varieties of tiny microoganisms which play a great role in humans and plants lives, for balancing the ecosystem, food processing, water cleaning processes and promoting growth of plants BIB001 . Some are also harmful to human health, environments and other living organisms. In this review, recent microorganism image segmentation techniques are introduced. It is noted that there exist some The associate editor coordinating the review of this manuscript and approving it for publication was Habib Ullah. review works on image segmentation techniques, for example,  - BIB002 and BIB003 . However, these reviews are based on segmentation of non-microorganism images except only in our previous review in BIB003 , which gives overview on microorganism image analysis (partially, including segmentation). Furthermore, no concrete conclusions are pinpointed on the best segmentation methods for particular microorganism segmentation challenges or most frequently used ones. Thus, we attempt to fill this interesting research blank by a review of around 85 works. VOLUME 7, 2019 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/