Predicting fertilizer treatment of maize using decision tree algorithm - Nusrat Jahan; Rezvi Shahariar (Institute of Advanced Engineering and Science, 2020)
Machine learning approaches are progressively successful in image based analysis such as different diseases prediction as well as level of risk assessment. In this paper, image based data analysis with machine learning technique was applied to fertilizer treatment of maize. We address this issue as our country depend on agricultural field rather than others. Maize has a bright future. To predict fertilizer treatment of maize dataset was comprised of ground coverage region which highlights the green pixels of a maize image. For calculating green pixels from an image we used “Can Eye” tool. The achievement of machine learning approaches is highly dependent on quality and quantity of the dataset which is used for training the machine for better classification result. For this perseverance, we have collected images from the maize field directly. Then processed those images and classified the data into four classes (Less Nitrogen=-N, Less Phosphorus=-P, Less Potassium=-K and NPK) to train our machine using decision tree algorithm to predict fertilizer treatment. We have got 93% classification accuracy for decision tree. Finally, the outcome of this paper is fertilizer treatment of a maize field based on the ground cover percentage, and we implemented this whole paper work using an android platform because of the availability of android mobile phone throughout the world.