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Agriculture is critical to India's economy. The most serious threat to food stability is population growth. Population growth increases demand, forcing farmers to produce more to keep up. Crop yield prediction technology can help ranchers increase productivity and efficiency. For the cultivation of oilseed crop yield, proper manure rates are required. When nutrients are scarce or over-fertilized, yields suffer and the environmental burden increases. To address these concerns, our proposed work employs machine learning techniques to predict the yield of oilseed crops grown with organic manure, as well as the amount and type of agricultural manure to be used for a specific crop in various districts of Tamil Nadu. The training set includes actual yield data from 1961 to 2007, while the validation set includes data from 2008 to 2019. The results of the proposed algorithm are compared to those of other machine learning algorithms, namely bagging, random forest, linear regression, and naive bayes which have accuracy rates of 98.5%, 96.5%, 94.5%, and 92.5%, respectively. According to the study, bagging (Bootstrap Aggregation) outperforms other algorithms for crop yield prediction, whereas boosting algorithms outperform other algorithms for recommendation systems to determine which crop to plant, which type of organic manure to use, and how much manure to use in a specific area and time.