The main goal of this investigation is to enhance the precision of crop forecasting by transitioning from the Adaboost method to Naive Bayes analysis. The current inadequacy in food production and forecasting is attributed to anthropogenic climate change, resulting in low supply. This has significant adverse effects on farmers’ economic conditions due to poor yield and the challenges associated with predicting future crops. This research initiative aims to assist novice farmers by leveraging machine learning to recommend suitable crops. The approach involves aggregating information on crop seeds along with specific requirements for optimal growth, encompassing nitrogen and phosphorus levels, temperature, humidity, pH, and rainfall. Statistical analysis, employing a t-test for independent samples with a significance level of 0.001 (p<0.05), G-power of 0.8, mean, and standard deviation, was conducted on data collected from diverse sources. In this experiment, a total of 20 samples were gathered and divided into two groups of 10 each. Group 1 utilized the Innovative Naive Bayes technique, while Group 2 employed the Adaboost methodology. The data analysis revealed that the Naïve Bayes Algorithm exhibited high accuracy (99.39%) compared to the Adaboost algorithm (79.45%) with a significance level of the independent sample t-test at 0.001 (p<0.05). This suggests that the Gaussian Naive Bayes algorithm is more accurate in predicting crop outcomes than the Adaboost algorithm. Key terms associated with this study include Crop Prediction, Crops, Adaboost Algorithm, Innovative Naive Bayes Algorithm, Machine Learning, Crop Yield Forecasting, and Maize.
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