Optimized Deep Learning Methods for Crop Yield Prediction  

作　　者：K.Vignesh A.Askarunisa A.M.Abirami 

机构地区：[1]KLN College of Information Technology,Pottapalaiyam,630612,India [2]Sethu Institute of Technology,Kariyapatti,626115,India [3]Thiagarajar College of Engineering,Madurai,625015,India

出　　处：《Computer Systems Science & Engineering》2023年第2期1051-1067,共17页计算机系统科学与工程（英文）

摘　　要：Crop yield has been predicted using environmental,land,water,and crop characteristics in a prospective research design.When it comes to predicting crop production,there are a number of factors to consider,including weather con-ditions,soil qualities,water levels and the location of the farm.A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting.The combination of data mining and deep learning creates a whole crop yield pre-diction system that is able to connect raw data to predicted crop yields.The sug-gested study uses a Discrete Deep belief network with Visual Geometry Group(VGG)Net classification method over the tweak chick swarm optimization approach to estimate agricultural production.The Network’s successively stacked layers were fed the data parameters.Based on the input parameters,a crop produc-tion prediction environment is constructed using the network architecture.Using the tweak chick swarm optimization technique,the best characteristics of input data are preprocessed,and the optimal output is used as input for the classification process.Discrete Deep belief network with the Visual Geometry Group Net clas-sifier is used to classify the data and forecast agricultural production.The sug-gested model correctly predicts crop output with 97 percent accuracy,exceeding existing models by maintaining the baseline data distribution.

关 键 词：Data mining deep learning crop production tweak chick swarm optimization algorithm discrete deep belief network with VGG Net classifier 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]