# Prediction of Harvest Time of Apple Trees: An RNN-Based Approach
## 2 Related Works
[20], a hybrid method is proposed combining neural networks with integrated learning to predict the flowering period of \"Red Fuji\" apple trees. The method uses LSTM networks and the Random Forest and Adaboost classification functions. The proposed model has high applicability and accuracy for flowering prediction. [PERSON] et al. [26] developed a wheat yield production forecasting model using LSTM networks. The objective focuses on the development of a wheat yield prediction model using Robust-LOWESS as a smoothing function in conjunction with an LSTM neural network model. The results show that the proposed model achieves a good performance in terms of productivity prediction. Aiming at a comparative analysis between the analyzed works, Table 1 presents the main characteristics of each approach. All works present some solutions to related problems. Although they do not have the same focus, they have a solid relationship with interest. The Culture column shows the work related to the apple tree culture. The ML column shows works that use ML techniques. The Other approach column shows works that use some other strategy to predict phenological stages and harvest prediction. The ANN and RNN columns present jobs that use these techniques. The literature review resulted in a set of articles that focus on harvest time prediction initiatives and others related to predicting the phenological stages in general of apple trees and other crops. Among the works, we can emphasize some characteristics. We see a solid tendency to assess the impacts of rising global temperature and climate change on plant phenology. We observe works with approaches in the sense of analysis or even making the function of phenology as a function of climate or other factors that alter phenomena. Many jobs are related to the cultivation of wheat, soybeans, and corn. We observe that a large part of the work related to harvest forecasting is focused on yield forecasting. Although there are many solutions related to the prediction of phenological stages, we observed a gap regarding the use of computational intelligence for a prediction start date of the harvest of apple trees. We did not find an approach similar to the one proposed in this work. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference(s)**} & \multirow{2}{*}{\begin{tabular}{c} **Culture** \\ **(Apple)** \\ \end{tabular} } & \multirow{2}{*}{**ML**} & \multirow{2}{*}{**ANN**} & \multirow{2}{*}{**RNN**} & \multirow{2}{*}{\
\begin{tabular}{c} **Other** \\ **Approach** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Predict** \\ **Harvest** \\ **Date** \\ \end{tabular} } \\ \hline
[18] & ✓ & & & & & ✓ & ✓ \\
[19] & ✓ & & & & ✓ & \\
[20] & ✓ & ✓ & & ✓ & & ✓ & \\
[21] & ✓ & & & & & ✓ & \\
[25] & & ✓ & & & & & \\
[24] & & ✓ & ✓ & & & & \\
[23] & ✓ & & & & ✓ & & \\
[26] & & ✓ & & & ✓ & & \\
[28] & & ✓ & & & ✓ & & \\
[1] & ✓ & & & & ✓ & & \\
[16] & ✓ & & & & & ✓ & \\
[22] & ✓ & & & & & ✓ & \\
[29] & ✓ & ✓ & ✓ & & & \\
[27] & & ✓ & ✓ & & & \\
[17] & ✓ & ✓ & ✓ & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative table of approaches in the related work.