Determination of the phenological stages of plants is important for the growth of healthy and productive plants. The knowledge of transition times of phenological stages of a plant can provide valuable data for planning, organizing and timely execution of agricultural activities (spraying, irrigation etc.). TARBIL is an agricultural monitoring and information system that is founded and supported by Republic of Turkey Ministry of Food, Agriculture and Livestock. This system has a network of stations located in many parts of Turkey. Stations, that contain many sensors and cameras, periodically collect images and meteorological data from the agricultural fields. Previous works focus on either only about plant identification or only phenological stage recognition using only one texture analysis method. Our approachment to the problem is novel because not only the recognition of the plant type or the recognition of only the phenological stage, but also joint identification of the plant type and the phenological stages are provided with several texture and color feature analysis methods. In this work, a study is conducted to compare the use of several image texture features along with color features extracted from TARBIL field image data for the classification of the plants and their phenological stages. Experimental results show that HOG (Histograms of Oriented Gradients) yields the best performance among the texture features tested.