# Crop Prediction Model Using Machine Learning Algorithms
## 7 Conclusions  
Our research highlighted the significance of incorporating machine learning algorithms and IoT sensors in modern agriculture to optimize reap production and reduce waste through informed decision-making. This study identifies the challenges and opportunities associated with integrating these technologies in agriculture. It presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms along with accuracy, error values, build, and test time for each classification algorithm. The findings suggest that analyzing wide-ranging data collected from farms, including real-time data from IoT sensors, can enable farmers to make more informed decisions about factors that affect harvest growth. Despite the challenges associated with deploying machine learning in agriculture, our results achieved so far are very promising in that machine learning approaches will become increasingly crucial for production predictions in agriculture in the future. In this experiment, crops were investigated according to general characteristics using different machine learning algorithms, and valuable results were obtained by making predictions in cases where certain crop types are unknown or cannot be easily identified. Our work indicated that appropriate feature selection is critical to achieve better accuracy in machine learning algorithms while analyzing agricultural data. Using the Temperature, Humidity, pH, and Precipitation features in the dataset, it achieved the highest accuracy, reaching 97.05% with Bayes Net and 97.32% with Random Forest. This research provided valuable insights into the potential benefits of these technologies in modern agriculture, and further research and development in this field could help optimize crop production, reduce waste, and improve food security globally. In future work, more crop data will be evaluated using GPS-based IoT and sensor data from different geographic regions. All these results will be analyzed using a machine learning algorithm. Thus, our data evaluation pool will be established. In addition, different species of the same plant variety will be analyzed separately, and it will be possible to reveal which product is the best type of product among the same species using different machine learning algorithms. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Accuracy** & \begin{tabular}{c} **Growth** \\ **Characteristics** \\ \end{tabular} & **Usage** & **Type** & \
\begin{tabular}{c} **Water** \\ **Requirements** \\ \end{tabular} &
\begin{tabular}{c} **Harvest** \\ **Method** \\ \end{tabular} \\ \hline Bayes Net & 99.59 & 96.79 & 91.31 & 99.13 & 85.69 & 89.17 \\ \hline Naive Bayes Classifier & 99.46 & 79.41 & 85.69 & 90.59 & 65.90 & 76.33 \\ \hline Logistic & 97.99 & 83.28 & 86.76 & 91.04 & 80.62 & 66.57 \\ \hline Multilayer Perception & 98.79 & 97.99 & 98.12 & 97.41 & 87.16 & 95.72 \\ \hline Simple Logistic & 98.66 & 82.08 & 87.71 & 90.91 & 80.08 & 67.51 \\ \hline IBK & 97.86 & 98.53 & 98.66 & 98.72 & 97.99 & 97.99 \\ \hline KSTAR & 97.86 & 99.19 & 99.19 & 98.86 & 97.86 & 97.86 \\ \hline LWL & 76.74 & 83.02 & 88.23 & 67.27 & 57.75 & 70.18 \\ \hline Ada BoostM1 & 6.82 & 76.87 & 82.08 & 45.32 & 44.11 & 61.23 \\ \hline Regression & 98.38 & 99.19 & 99.06 & 99.09 & 98.93 & 98.93 \\ \hline Decision Table & 88.50 & 96.12 & 95.58 & 95.04 & 93.04 & 94.65 \\ \hline Hoeffding Tree & 99.46 & 79.41 & 85.43 & 89.82 & 66.31 & 76.60 \\ \hline J48 & 98.79 & 98.26 & 97.86 & 98.63 & 98.39 & 99.33 \\ \hline Random Forest & 99.46 & 99.33 & 99.73 & 99.45 & 99.73 & 99.59 \\ \hline Random Tree & 98.12 & 98.66 & 99.33 & 98.36 & 97.59 & 98.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the accuracies (%) of various classification methods.