Ashraf Darwish
2018 Journal of the Egyptian Mathematical Society  
Plant recognition and diseases identification have an impact on the sustainable development of many countries in the agricultural sector. The automatic plant recognition and diseases identification will assist the specialists and experts in agriculture to overcome many of plant diseases and problems. The automation of plant diseases identification and recognition approaches have received considerable interest in the last years because their effect on the growth of the economy of countries,
more » ... of countries, which may depend mainly on agriculture and to reduce the economic losses in the sustainable agriculture industry in general. However, human cognition and sight are not sufficient to identify the region of interest in the images of plants, usually, stems and leaves. Nowadays, image-based methods are considered as a visual assisting of plant recognition and diseases identification with the aid of the recent advances in image processing area. In this paper, we describe and analyze the automated image-based methods and discuss the state-of-art of plant recognition and diseases identification that has been applied in the last years. Also, we explore the role of image processing methods and classifiers in plant diseases identification and recognition. Different types of datasets of plant diseases identification and recognition are introduced briefly with their existing problems. As an example, the preprocessing phase of this issue is implemented based on real infected tomato leaves. Also, shape feature, color feature, and texture feature have been reviewed. Moreover, we described the important classifiers that are used currently used in the classification process. Also, hybrid classifiers can integrate the results from multiple algorithms with the aim of improving classification accuracy. Therefore, some of the well-known hybrid classifiers for plant diseases identification and recognition have been presented. Some solutions of using image-based methods such as complex backgrounds of the region of interest, different plant diseases can produce similar symptoms, and the conditions of capturing images have been presented. Finally, some points of the future work are proposed.
doi:10.21608/joems.2018.2671.1021 fatcat:kjialq6pnfhwhoalej7ppwd3xy