This paper proposes methods to classify the plants using images taken from agricultural lands. Wheat, maize and lentil images are used. Texture features of agricultural land images are obtained using Gray Level Co-occurrence Matrix (GLCM) and Laws' Texture Energy Measures which are two of texture analysis methods. The texture features vectors which are generated with these two methods are classified with different classifiers. Agricultural land images are separated to three different classes using k-Nearest Neighbors (k-NN) algorithm, Support Vector Machines (SVM) and Naive Bayes Classifiers. It is understand that Gray Level Co-occurrence Matrix and Laws' Texture Energy Measures are sensitive to field images. Classification of Laws' Texture Energy Measures data yields 100% performance in k-Nearest Neighbors and Support Vector Machines methods. Laws' Texture Energy Measures yield better performance than Gray Level Co-occurrence Matrix.