Crop Yield Prediction Using Deep Neural Networks

Crop yield prediction with the help of (DNNs) deep neural networks is a trustworthy application in machine learning of agricultural field. Start your research work off on the right foot by having our PhD professionals help you complete your thesis and dissertation. We have a team of experts who specialize in original topics such as Crop yield prediction using deep neural networks. Don’t waste time searching for research topics when you can have the guidance and expertise of our professionals. Let us lend a helping hand to make your work stand out. Depending on past data and real-time information, DNNs observe the critical patterns in huge datasets and make perfect predictions.

The process of creating a crop yield prediction project using deep neural networks is described below,

Data Collection

1.Historical Data :

The past data on crop yields is gathered by us that involve the information like weather patterns, soil quality, crop type and other similar factors. This datasets are occupied for training process of deep neural network.

2.Real-time Data:

The real-time data includes data sources like weather forecasts, satellite imagery and sensor data for the accurate forecasting and related to the current trends.

Data Preprocessing :

1.Data Cleaning :

Our dataset is purified by managing the missing values, anomalies and contradictions.

2.Feature Engineering :

Through fresh data, the common features is extracted which helps the neural network for learning the best patterns. It involves data supplementary, building interaction conditions and transforming variables.

3.Model Selection :

The appropriate deep learning architectures for the topic are selected by us.  It is frequently deployed for time-series data like Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) whereas Convolutional Neural Networks (CNNs) are efficient for image data like satellite imagery.

4.Model Design :

Input Layer:

Depends on the features of dataset, we specify the input layer.

Hidden Layers:

We sketch the structure of hidden layers. The number of layers and neurons in each particular layer is resolved by research and enhancements.

Output Layers:

The output layer is defined with a single neuron for performing regression task (prediction of yield).

5.Model Training :

Split Data :

Dataset is separated into training, validation and test sets.

Normalization :

Numerical features are standardized for a similar scale that advances the training firmness.

Training :

By utilizing the training dataset, we train the model. The validation set is occupied for observing the model performance and protect from Overfitting.


Tuning :

For upgrading the performance of model, the various hyperparameters like learning rate, batch size are verified by us.

7.Evaluation :

In the testing set, our model is being explored for estimating its generalization performance.

8.Deployment :

Once we satisfied with the model performance, and then apply its in real-time predictions. Constantly upgrade with novel data by merging with this system.

9.Monitoring and Maintenance:

The model performance is frequently observed by us and improved as it is required for latest data or developed algorithms.

Recollect that the attainability of project is mostly based on the features and amounts of available data. In addition to, it depends on the accuracy of the selected model. Moreover, collaborating with agricultural field experts, develops the understanding process and for the perfect prediction.

Crop Yield Prediction Using Deep Learning

Crop Yield Prediction Topics

The following are the topics that we have recently developed using Crop yield prediction using deep neural networks have a look at it and go through our work, stay updated on our work. Call to our technical team as we share more information for all your research queries.

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  24. A Comprehensive Review on Deep learning Techniques for Crop Yield Prediction
  25. Comparison Analysis of Oil Crop Yield Prediction in Magway Region Using Machine Learning Method
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  31. Machine Learning Algorithms for Crop Yield Prediction in Real-Time Scenarios
  32. Crop Yield Prediction Based on Machine Learning Techniques
  33. A Comparative Analysis of Regression Models for Crop Yield Prediction Based on Rainfall Data: Experimental Study and Future Perspective
  34. Machine Learning Techniques for Weather based Crop Yield Prediction
  35. A Comparative Study of Agricultural Crop Yield Prediction Using Machine Learning Techniques
  36. TFSF: Topological and Feature Space Fusion with Spatio-Temporal Modeling for Crop Yield Prediction
  37. Transfer Learning Techniques For Improving The Quality Of Prediction In Crop Yield
  38. Weather based crop yield prediction using artificial neural networks : A comparative study with other approaches
  39. A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction
  40. Deep Neural Network Model for Proficient Crop Yield Prediction

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