| Srilakshmi A., Madhumitha K., Geetha K[4] | SVM decision tree (Hybrid approach), Random Forest | Temperature, Humidity, pH, rainfall, label | SVM decision tree (Hybrid approach)- 91.8%.Random Forest - 95% | sugarcane, coconut, jute, cotton, papaya, groundnut, maize, graphs, rice, mango, rubber etc | Predict crop for any type of field | Small dataset |
| R. Pallavi Reddy, B. Vinitha, K. Rishita, K. Pranavi [2020] [2] | Linear Regression Model | N, P, K, and moisture values |
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| generate recommendations to improve crop production and estimates the price of the yield | Limited in capturing non-linear patterns, Assumes homoscedasticity and independence of errors |
| S. Mamatha Jajur, Soumya N. G. [2019] [5] | KNN, Decision trees, SVM, CNN and LSTM, ANNs, K-means clustering | Soil Type, pH value, NPK content of the soil, Water holding, Temperature, Average rainfall, Previously Harvested crop | - | wheat, rice, bajra, maize, jawar, | select the optimum crop while keeping a number of variables in mind to boost the output of agriculture, minimise the deterioration of the soil in fields that are under cultivation and use less fertiliser when growing crops. | Many algorithms are used |
| Mr. Santosh Mahagaonkar, Devdatta A. Bondre [2019] [6] | Random Forest, Support Vector Machine algorithm | crop, crop yield dataset, Location, soil and crop nutrients, fertilizer datasets | soil classification, RF-86.35% crop yield prediction SVM -99.47% | Soybean, Rice, Jowar, Wheat, Sunflower, Cotton, Sugarcane, Tobacco, Onion, Dry Chili, etc. | future prediction of crop yield | Low accuracy in soil classification performance heavily depends on parameter tuning and it is memory intensive, particularly for large datasets |
| D. Anantha Reddy, Bhagyashri Dadore, Aarti Watekar [2019] [7] | Naïve Bayes, K-NEAREST NEIGHBOUR, RANDOM FOREST, CHAID | Depth, Texture, pH, Soil Colour, Permeability, Drainage, Water holding and Erosion | - | groundnut, pulses, cotton, vegetables, paddy, sugarcane, coriander. | Assist farmers in planting the appropriate seed according to the needs of the soil in order to boost output. | The Naïve Bayes algorithm pretends feature independence, which might not be true when dealing with real-world data., CHAID - Limited to categorical target variables and predictors, making it less versatile for handling continuous data |
| Nidhi H. Kulkarni [8] 2018 | Linear SVM algorithms, Random Forest, Naïve Bayes | Soil type, pH soil, NPK, average rainfall, porosity of soil, sowing season temperature | 99.91% | Cotton, Sugarcane, Rice, Wheat | Crop productivity has improved exponentially for rice, wheat, cotton, and sugarcane. | restricted to a fairly small number of crops |
| Zeel Doshi [3] 2018 | Neural Network Random Forest, Decision Tree, KNN | Temperature rainfall, Location, soil condition | 91% | Jute, sesame, soybean, sugarcane, tobacco, sunflower seeds, ragi, potato, tur, grapeseed, and mustard, bajra, maize wheat, rice gram, barley, cotton, groundnut, and pulses | Neural Networks have the highest accuracy percentage. | predict the crop using the harvest from the previous cycle. Crop supply and demand are not considered |
| Rohit Kumar Rajak [9] 2017 | Random Tree, NB-classifier, ANN, SVM | depth, pH, texture, permeability to store water, color ofthe soil, and drainage from erosion | - | vegetables, rice, sugarcane, sorghum, coriander, bananas, legumes, and groundnuts | boosts agricultural productivity | larger dataset for model training |
| S. Pudumalar [10] 2016 | Random Tree, Naïve Bayes, KNN, CHAID, | Depth, pH, texture, waterholding permeability, Soil color, erosion drainage, | 88% | millet, pulses, groundnut, cotton, banana, vegetables, paddy, sugarcane, sorghum, coriander | Boost productivity | larger dataset for model training |
| Rakesh Kumar [11] 2015 | CSM, Gradient Boosted Decision Tree, and Greedy Forest | soil type, weather, crop type, water density, |
| ratoi, toria, wheat, potato, sarso, linseed, masoor, khesari, onion, sugarcane, Kanda, mung, til, pumpkin, nenua, ladies’ finger, rice, soybean, sweet potato, toor, vegetable seed, and so on | offers a method to select crops while taking into account the yield forecast rate influenced by various factors. | Adopting a prediction technique that performs well and has greater accuracy is necessary |
