• A.KRISHNAVENI
• K.Vanitha
• K.M.Buvaneswari

Prompt and optimized classification of soil type with the suitable crop is a crucial problem-solving in the agriculture field. All the crofters needed beneficiary outcomes through the cultivation of crops done in peculiar soil. Choosing the appropriate soil type and the apt crop is successful only when the crofter has the awareness and prefers the suitable methods. Soil consists of nutrients, which are used by the plants to grow. Variety of soil and various properties of those soils gave an opportunity to the crofter to choose one particular soil with different types of crop cultivation. Particular land type is the primary concern for satisfactory outputs from crop cultivation. To increase crop cultivation every farmer should be aware to make decision-making about soil and crop. This can be done by first analyzing the soil then classifying it into different soil groups. Based on these soil groups, one can decide which crop is best suited and is beneficial. Based on the crofter skill and knowledge, the land was chosen for soil classification. This work proposes a novel approach to select appropriate features from a data set for crop prediction. The experimental results show that the Naïve Bayes technique helps accurately predict a suitable crop. The performance of the proposed technique is evaluated by various metrics such as accuracy (ACC), precision, recall, specificity, and F1 score, mean absolute error, and time is taken. From the performance analysis, it is justified that the proposed technique performs better than other methods.

Optimization, Soil, Crop, Classification, Accuracy, Naïve Bayes, F1 measure, Recall, Precision

"OPTIMIZED CROP PREDICTION USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 12, page no.b667-b671, December-2021, Available :http://www.jetir.org/papers/JETIR2112177.pdf

"OPTIMIZED CROP PREDICTION USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 12, page no. ppb667-b671, December-2021, Available at : http://www.jetir.org/papers/JETIR2112177.pdf