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With Kotlin becoming a viable language replacement for Java, there is a need for translators and data flow analysis libraries to create maintainable and readable source code. Instagram, Uber, and Gradle are only a few of the large corporations that have either switched from Java to Kotlin completely or started to use it in internal tools in order to reduce code base size. Developers have claimed that Kotlin is fun to use in comparison to Java and much of the boilerplate code is reduced. With Java being the main language for the open source organization, PhenoApps, there is a need to support both Java and Kotlin to increase the maintainability of the code. Fortunately, JetBrains has an open-source IDE plugin for translating Java to Kotlin; however, the translation has some fundamental issues which shall be discussed further in this paper. Introducing, j2k, a CLI translation tool which includes various anti-pattern detection for syntactical formatting, performance, and other Android requirements. The new tool introduced within this paper, j2kCLI allows users to directly translate strings of Java code to Kotlin, or entire directories. This facilitates the maintainability of a large open source code base. 

Living in a data-driven world with rapidly growing machine learning techniques, it is apparent that utilizing these methods is necessary to achieve state-of-the-art performance in object detection. Recent novel approaches in the deep-learning field have boasted real-time object segmentation methods given the algorithm is connected to a large validation dataset. Knowing that these algorithms are restricted to a given dataset, it is apparent that the need for data generating algorithms is on a rise. As some object detection problems may suffice with a statically trained deep-learning model, it is true that others will not. Given the no free lunch theorem, we know that no machine learning algorithm can truly generalize to data it has not been trained on; therefore, deep learning models trained on images of cats will not necessarily classify dogs correctly. With modern deep learning libraries being ported for mobile devices, a wide range of utilityhas been made apparent for plant researchers around the world. One such usage of these real-time approaches is to count and classify seed kernels, replacing monotonous-human-error-ridden tasks. Plant scientists around the world have daily jobs of counting seeds by hand or using multi-thousand dollar devices to automate the task. It is apparent that many third world countries, where such consumer devices do not exist or require too many resources, could benefit from such an automated task. PhenoApps, an organization started within Kansas State University, has been supplying a subset of these countries with modern phones for such uses. With the following seed segmentation algorithm and the usage of modern mobile devices, scientists can count seeds with the click of a button and produce results in split-seconds. The algorithms proposed in this paper achieve multiple novel implementations. Mainly, Rice’s Theorem was used to show that object detection in clusters is an undecidable task for Turing Machines. Along with this, the novel implementations include an Android application which can segment seed kernels and a machine learning algorithm which can accurately generate contour data sets. The data generator provided in this paper is an effective start for the later usage of deep learning models and is the first step for a real-time dynamic and static seed counter.