Abstract
In India, agribusiness-related ventures are the significant wellspring of living for the individuals. It is one of the nations which experience the ill effects of characteristic disasters like dry season or flood which harms the harvest. This prompts tremendous money-related misfortune for the nation. Individuals of India have been rehearsing farming for quite a long time, yet the outcomes are failing to satisfy because of different variables that influence the harvest yield. Predicting the crop yield in advance requires an efficient investigation of gigantic information originating from different factors like soil quality, pH, N, P, K and so on for storing, selling, pricing and imports exports, etc. Through data mining, insights can be drawn by analyzing the huge volume of data and draw very important and conclusions for any year yield. The prediction of any crop yield majorly depends on accuracy of the extracted features and how appropriately classifiers have been employed.
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Chaitra, H.V., Ramachandra, Sah, C., Pradhan, S., Kuralla, S., Sree, V. (2021). An Efficient Data Mining Algorithm for Crop Yield Prediction. In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 190. Springer, Singapore. https://doi.org/10.1007/978-981-16-0882-7_19
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