# A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study
## 7 Discussion and Conclusions  
In this work, we proposed a multiple-input neural network model, called MNN, for the prediction of cotton's yield. The proposed model uses as inputs three different kinds of data (soil, cultivation management, and yield management) which are treated and handled independently. A significant advantage of the selected architecture is that it is able to efficiently exploit information in mixed data, since these kinds of data usually require being processed separately. Additionally, the proposed architecture is superior to the traditionally fully connected neural network architecture in terms of flexibility and adaptivity for low computational cost. An empirical study was performed utilizing data from three consecutive years from cotton farms in Central Greece, in which the prediction performance of the proposed model was evaluated against that of traditional neural network-based and other state-of-the-art models. Our numerical experiments revealed the superiority of the proposed approach, providing empirical evidence that agricultural datasets, which consists of different types of data, should be treated utilizing the proposed approach. Additionally, the performance of the proposed model was evaluated utilizing three different types of hidden layers, i.e., dense, LSTM, and GRU. It is worth noticing that the utilization of the recurrent layers benefitted the performance of MNN considerably, compared with the utilization of traditionally  
Figure 3: Box-plot for the performance of the proposed MNN model and the state-of-the-art prediction models based on (**a**) MAE; (**b**) RMSE; (**c**) MAPE; (**d**) MAPE. dense layers. Furthermore, the proposed MNN model exhibited slightly better performance utilizing GRU layers instead of LSTM layers. A possible explanation for this is that each LSTM unit has more gates for the gradients to flow through, causing steady progress to be more difficult to maintain [22; 31]. In addition, by comparing the performance of MNN\({}_{2}\) and MNN\({}_{3}\), we can conclude that the vanishing gradient problem was rarely or not occurring in our experiments. This is probably due to the utilization of the ReLU activation function and to the \"sparse\" architecture of the proposed multi-input neural network model. More analytically, the utilization of ReLU activation function is able to frequently prevent the vanishing gradient problem from occurring, since it only saturates in one direction, while the \"sparse\" architecture of the proposed model makes it considerably less complicated compared to a fully connected neural network. Another possible reason could be the complexity of the utilized dataset as well its relative small number of training instances. To this end, more experiments utilizing more cotton and other crop datasets are needed, which is definitely included in our future research. Furthermore, it is worth mentioning that the features used in this research do not constitute a conclusive list. An extension could introduce new features and other criteria, which may potentially influence the prediction performance. Clearly, it is still under consideration which feature has a greater impact for predicting cotton production or which of them should be used by an intelligent model. These questions constitute an interesting aspect for future research. Nevertheless, it is likely that the research to answer these questions could reveal additional information about the cotton yield behavior. A limitation of this work is that the utilized dataset contained only 349 samples. Based on the preliminary experimental results, it seems that the proposed approach is able to develop a reliable and accurate model. However, we intend to enlarge our database with data from more sample sites and more years in order to perform a exhaustive performance evaluation of the compared models on various datasets as well as a comprehensive statistical analysis (use of a nonparametric test and/or a post-hoc tests).