Determining the Yield of the Crop using Artificial Neural Network Method
Ritesh Kumar Ranjan1, Priyasha Nandi2, Yugit Chauhan3, J.Jagadeesan4
1Ritesh Kumar Rajan, student in Dept of Computer Science & Engineering at SRM Institute of Science & Technology, Ramapuram.
2Priyasha Nandi, student in Dept of Computer Science & Engineering at SRM Institute of Science & Technology, Ramapuram.
3Yugit Chauhan, student in Dept of Computer Science & Engineering at SRM Institute of Science & Technology, Ramapuram.
4Dr. J Jagadeesan, professor in in Dept of Computer Science & Engineering at SRM Institute of Science & Technology, Ramapuram.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2959-2965 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1289109119/2019©BEIESP | DOI: 10.35940/ijeat.A1289.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The agricultural system is complex and comprehend since it deals with large data that comes from several factors. Lot of techniques and have been used to identify any interactions between factors affecting yields with crop performance. The major objective of this paper is to help us predict the yield of a particular crop before even cultivating it for its production. We are using artificial neural networks for forwarding and implementing a system that will help the farmers in finding their crop yields according to their given data as input in the system and the system will give output based on previous data. The method used in this crop yield system is an artificial neural network and the algorithm used is feed forward and back propagation. Provide the input of data sets and the desired outcome of the system. Compute the error between the actual and desired outcome of the system. Amendment of the weight associated with different inputs and different functions. Compare the errors and the tolerance ratio of the output. Various machine learning techniques have been used in the past for calculating the crop yield using remote data. However, these methods are less useful and effective for predicting the yield of maize and for some other crops, which is cultivated at different times in various fields. The major application of this crop yield system is that it will help us to predict the yield before even cultivating it by studying the previous data collected such as soil fertility, pH level.
Keywords: Artificial Neural Network, Agriculture, Back Propagation , Crop Production, Feed Forward.