# A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study
## 2 Related Work  
During the last decade, the significant advances in digital technology and machine learning have renewed the interest of the scientific community for the development of efficient expert systems for assisting agriculture precision and production. [PERSON] et al. [2] conducted an excellent review and presented in detail the recent developments performed within the last two decades, focusing on the application of machine learning methods for accurate crop prediction as well as the estimation of nitrogen status. The main findings of this research were that machine learning techniques provide complete and cost-effective solutions for efficiently estimating crop and environment state as well as significant assistance in decision-making. Additionally, the authors attempted to gain significant insights on crop prediction by identifying the factors which affect it, and possible directions to support and improve precision agriculture through artificial intelligence. To the best of our knowledge,the application of machine learning methods for predicting cotton crop has been limited compared to other types of crops. In the sequel, we briefly discuss some rewarding studies regarding cotton yield prediction using machine learning models. [PERSON] et al. [12] proposed an intelligent knowledge-based model for modeling the behavior of crop cotton yield in precision farming. The proposed model was based on using a soft computing methodology based on Fuzzy Cognitive Maps (FCMs) and on the unsupervised learning algorithm for FCMs for assessing measurement data and updating initial knowledge. The performance of the proposed model was extensively evaluated for 360 cases in a 5 hectare experimental farm for predicting the cotton yield using a two-level classification (\"Low\" and \"High\"). Moreover, their used data were collected from central Greece, during the years 2001, 2003, and 2006. Based on the brief experimental analysis, the authors stated that the proposed FCMs model is able to assist agriculture managers with better understanding cotton yield requirements. Along this line, in [11], the authors extended their previous work, including more elegant conditions to increase the efficiency of their intelligent model. Their numerical experiments reported that the updated model outperformed traditional prediction models such as artificial neural networks (ANNs), decision trees, and Naive-Bayes. Finally, the authors highlighted that the proposed updated model consists a convenient decision support tool for cotton production due to its sufficient simplicity and interpretability. [PERSON] et al. [13] studied the problem of classifying the quality of cotton's seed utilizing several growth stages of the crop. Their dataset consists of 900 records and 24 features from a set of different cotton categories. The authors conducted a performance evaluation of state-of-art prediction models, including decision trees, Naive-Bayes and ANNs for identifying the quality of cotton's seed (\"Good\", \"Average\" and \"Bad\"). Their experiments showed that ANNs and decision trees provided almost identical performance, reporting 98.58% classification accuracy. However, the decision trees reported significantly lower training time and computational cost. [PERSON] et al. [14] attempted to determine cotton lint yield in irrigated fields using remote sensing technology. More specifically, they utilized ANNs for extracting information from remotely-sensed crop indices in order to predict and map the cotton lint yield of a field in two successive cropping seasons. The data in their research were obtained after conducting an on-farm irrigation experiment on a property of 73 hectares in west Tennessee during the years 2013 and 2014. Their numerical experiments presented some interesting results, revealing that neural networks can efficiently exploit crop indices phenology for predicting crop yield.