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The occurrence of disease in plants might affect the crop production at a large scale, resulting into decline of the economic growth rate of the country. The disease in plants can be detected and treated at an early stage. Machine learning (ML), deep learning (DL), and computer vision-based techniques could play a pivotal role in detecting and classifying the diseases at an early stage. These approaches have even surpassed the human performance, as well as image processing based traditional approaches in the analysis and classification of plant diseases. Over the years, numerous authors have applied various image processing ML and DL techniques for the diagnosis of different ailments in plants that gives great hope to the farmers and landlords to cure the disease at an early stage. In this study, the authors addressed and evaluated the various currently existing state of art methods and techniques based on machine and deep learning. Besides, the authors have also focused on various limitations and challenges of these approaches that can explore greater possibly of these methods about their usability for disease detection in plants.

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Abstract-A semantic net can be used to represent a sentence. A sentence in a language contains semantics which are polar in nature, that is, semantics which are positive, neutral and negative. Neutrosophy is a relatively new field of science which can be used to mathematically represent triads of concepts. These triads include truth, indeterminacy and falsehood, and so also positivity, neutrality and negativity. Thus a conventional semantic net has been extended in this paper using neutrosophy into a Polar Fuzzy Neutrosophic Semantic Net. A Polar Fuzzy Neutrosophic Semantic Net has been implemented in MATLAB and has been used to illustrate a polar sentence in English language. The paper demonstrates a method for the representation of polarity in a computer's memory. Thus, polar concepts can be applied to imbibe a machine such as a robot, with emotions, making machine emotion representation possible.