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, 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.