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Objective: To improve the performance of an efficient satellite image based CCF (Color, Climate, Flow) approximation model is presented in this article. Method: We attempted for plant growth estimation and yield estimation using artificial neural networks. The model receives the satellite images and preprocesses to improve the quality of the image. From the quality improved image, the method extracts the color values. Further, the features like climate and flow features from the data set of the region have been extracted. Using these features, for different time window, the method generates number of neurons and initializes them with the set of features. The set of images from the data set are used to extract several features and used to train the network. The classification is performed according to the same set of features obtained from input satellite image and the other features of the region at current window. The method estimates CCF plant growth and yield support measures as result.Based on these values, the yield estimation is performed. Result: The proposed method improves the performance of plant growth up to 97.25 and the yield estimation performance is increased up to 985 which is higher than previous approaches. Provide comparative estimation. Novelty: The proposed CCF model consider color, climate and features in plant growth estimation which differ from other approaches by considering maximum features obtained from satellite images as well as features collected from ground truth in different time stamp. Keywords: Sugarcane yield; plant growth; ANN; CCF model; feature approximation; satellite images; CCF support

Real time satellite image based CCF approximation model for efficient sugarcane growth and yield estimation using artificial neural networks