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Optimized Deep Learning Methods for Crop Yield Prediction

K. Vignesh1,*, A. Askarunisa2, A. M. Abirami3

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: email

Computer Systems Science and Engineering 2023, 44(2), 1051-1067.


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 conditions, 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 prediction system that is able to connect raw data to predicted crop yields. The suggested 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 production 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 classifier is used to classify the data and forecast agricultural production. The suggested model correctly predicts crop output with 97 percent accuracy, exceeding existing models by maintaining the baseline data distribution.


Cite This Article

K. Vignesh, A. Askarunisa and A. M. Abirami, "Optimized deep learning methods for crop yield prediction," Computer Systems Science and Engineering, vol. 44, no.2, pp. 1051–1067, 2023.

cc 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.
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