# A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study  
[PERSON]  
1 Department of Mathematics, University of Patras, GR 265-00 Patras, Greece  
Core Innovation and Technology O.E, GR 11745 Athens, Greece. Department of Crop Science, Agricultural University of Athens, GR 118-55 Athens, Greece;  
[EMAIL_ADDRESS] (S.D.D. ); [EMAIL_ADDRESS] (G.K.P.) Department of Natural Resources Management & Agricultural Engineering, Agricultural University of Athens, GR 118-55 Athens, Greece;  
Correspondence: [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Crop Science, Agricultural University of Athens, GR 118-55 Athens, Greece;  
[EMAIL_ADDRESS] (S.D.D. ); [EMAIL_ADDRESS] (G.K.P.) [PERSON]  
3 Department of Crop Science, Agricultural University of Athens, GR 118-55 Athens, Greece;  
The traditional way to forecast cotton production is mainly based on the empirical knowledge of the farmer or mostly by the agricultural expert [6; 7; 8]. Cultivation and weather information are processed by agriculturalists who attempt to perform accurate predictions about future yield production. Nevertheless, the quantity of cotton production possesses, by nature, nonlinear behavior since it is affected by several soil and climate factors and it is characterized by by spatio-temporal variability [9; 10]. As a result, the problem of conducting accurate predictions is considered a considerably hard problem. Moreover, the fact that yield monitors revealed that the cotton's production was different even in different parts of the same field [11] makes this prediction problem even more challenging. Therefore, the use and the development of sophisticated decision support tools is considered essential for potentially assisting agricultural investors and farmers gaining significant profits. During the last decade, the application of artificial intelligence techniques and methodologies sparked the interest of the scientific community, since they constitute the appropriate tools to deal with the noisy and sometimes chaotic nature of cotton's yield production and lead to more accurate predictions. Along this line, research focused on the development of expert systems for the prediction of agriculture production for assisting growth operations [2]. These intelligent systems exploit the high predictive ability of machine learning algorithms, focusing on increasing crop's efficiency and economic benefits, while simultaneously reducing risks and losses [11; 12; 13; 14; 15]. In this work, we propose a new neural network model for predicting cotton production, which is based on a multiple-input architecture and constitutes the main contribution. The proposed model utilizes as input three different kinds of data, namely soil data, cultivation management data, and yield management data. To the best of our knowledge, this is the first approach which utilizes three different types of features. The selected neural network architecture provides that each kind of input data are processed and handled in a different and independent way. The motivation behind our approach is to develop a learning system which is capable of efficiently exploiting information from different kinds of data, since these kinds of data usually require separate treatment. A series of experiments was conducted for evaluating the prediction performance of the proposed model by comparing it against traditional neural network-based and other state-of-the-art models. For our experiments, we utilized data from three consecutive years from cotton farms in Central Greece (Thessaly). The presented results demonstrate the prediction accuracy of the proposed model, providing empirical evidence that the proposed approach is able to develop an accurate and reliable model. The remainder of this paper is organized as follows: Section 2 presents a brief survey of rewarding studies, regarding the application of machine learning methodologies for cotton prediction. Section 3 presents a detailed description of the utilized data as well as the data preparation process.