Optimized Deep Learning Methods for Crop Yield Prediction K. Vignesh 1,* , A. Askarunisa 2 and A. M. Abirami 3 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 *Corresponding Author: K. Vignesh Email: vignesh.vkv2@gmail.com Received: 18 October 2021; Accepted: 11 January 2022 Abstract: 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 classi fication 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. Keywords: Data mining; deep learning; crop production; tweak chick swarm optimization algorithm; discrete deep belief network with VGG Net classifier 1 Introduction Crop production is influenced by a number of factors, including crop genotype, environmental conditions, and management practices. Over the years, seed companies have made significant strides forward in the field of crop genotyping. Changing climatic conditions, both in spatial and temporal, may result in a broad variety of agricultural yields from one year to the next. Highly precise yield forecasting is extremely helpful in these situations for global food production. Accurate projections can inform import and export decisions. Consequently, farmers may utilize the anticipated yield to make better management and financial decisions. Hybrids may be expected to operate well in new and untested environments. Predicting crop yields with any degree of accuracy is almost impossible due to the sheer This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computer Systems Science & Engineering DOI: 10.32604/csse.2023.024475 Article ech T Press Science