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Classification and feature extraction are other planned areas of study, since they build upon the basis of computer picture processing technology and have a direct impact on the results of image recognition. We compared low-level features, texture features, and form-based features to generate an original feature set from the input segmented crop photographs. We used the Histogram of Gradient (as colour-based features) to extract low-level features because of its ease of use and effectiveness. Within the realm of shape-based features, EHD was used to obtain defining properties. In order to have a better understanding of textures, we used GLCM to extract features based on them. When all features are added together, a feature vector with several dimensions is produced. To conclude, the data were classified using artificial neural networks (ANN). MATLAB is used for simulation purposes. The current difficulty is to …