A Neural Network Model for Predicting Cotton Yields
نویسندگان
چکیده
Predicting a realistic target yield is one of the critical problems in precision farming. An artificial neural network was employed to model the nonlinear relationship between cotton yield and the factors influencing yield. Using sixyear field data obtained from LuoYang Dry Land Research Center, the neural network model was developed and trained, and the RMSE for test data was 3.70%. The results indicate that the neural network model is a superior methodology for accurately setting cotton yields.
منابع مشابه
Using Artificial Neural Network Algorithm to Predict Tensile Properties of Cotton-Covered Nylon Core Yarns
Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile properties of co...
متن کاملUsing Artificial Neural Network Algorithm to Predict Tensile Properties of Cotton-Covered Nylon Core Yarns
Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile 
properties of ...
متن کاملArtificial neural network models for production of nano-grained structure in AISI 304L stainless steel by predicting thermo-mechanical parameters
An artificial neural network (ANN) model is developed for the analysis, simulation, and prediction of the austenite reversion in the thermo-mechanical treatment of 304L austenitic stainless steel. The results of the ANN model are in good agreement with the experimental data. The model is used to predict an appropriate annealing condition for austenite reversion through the martensite to austeni...
متن کاملYarn tenacity modeling using artificial neural networks and development of a decision support system based on genetic algorithms
Yarn tenacity is one of the most important properties in yarn production. This paper addresses modeling of yarn tenacity as well as optimally determining the amounts of the effective inputs to produce yarn with desired tenacity. The artificial neural network is used as a suitable structure for tenacity modeling of cotton yarn with 30 Ne. As the first step for modeling, the empirical data is col...
متن کاملPredicting Force in Single Point Incremental Forming by Using Artificial Neural Network
In this study, an artificial neural network was used to predict the minimum force required to single point incremental forming (SPIF) of thin sheets of Aluminium AA3003-O and calamine brass Cu67Zn33 alloy. Accordingly, the parameters for processing, i.e., step depth, the feed rate of the tool, spindle speed, wall angle, thickness of metal sheets and type of material were selected as input and t...
متن کامل