loukya2006/crop-_yield_prediction-with-cnn-bigru-and-pso-nspso

How loukya2006/crop-_yield_prediction-with-cnn-bigru-and-pso-nspso works

This repository is primarily a Karnataka crop-yield prediction research pipeline, with a minimal, empty React shell. The main workflow prepares the crop dataset, trains and compares CNN-BiGRU variants, and tunes hyperparameters using single- and multi-objective particle swarm optimization. Preprocessing artifacts and experiment outputs are persisted for evaluation and report figures. The Python pipeline appears in both the root src/ tree and crop_yield_project/src/; both are represented. The React app has no demonstrated product behavior. Relationships reflect the excerpts and source index; unsampled details are not inferred.

Main entry points: src/train.py Experiment orchestration · src/train.py Project experiments · src/preprocess.py Data preprocessing · src/preprocess.py Project preprocessing

Components 15

Data preparation

Karnataka crop data data/karnataka_multi_crop_master.csv
Data preprocessing src/preprocess.py
Root results artifacts results

Modeling and tuning

CNN-BiGRU model src/model_cnn_bigru.py
PSO and NSPSO src/pso_nspso.py

Experiment workflow

Experiment orchestration src/train.py

Evaluation and reporting

Evaluation figures src/evaluate.py

Project copy

Project crop data crop_yield_project/data/karnataka_multi_crop_master.csv
Project preprocessing crop_yield_project/src/preprocess.py
Project results artifacts crop_yield_project/results
Project CNN-BiGRU crop_yield_project/src/model_cnn_bigru.py
Project PSO and NSPSO crop_yield_project/src/pso_nspso.py
Project experiments crop_yield_project/src/train.py
Project figures crop_yield_project/src/evaluate.py

Other components

Researcher

Connections 19

Researcher → Experiment orchestration: runs experiments
Karnataka crop data → Data preprocessing: loads data src/preprocess.py
Data preprocessing → Root results artifacts: writes encoders and scaler src/preprocess.py
Experiment orchestration → Data preprocessing: uses preparation src/train.py
Experiment orchestration → CNN-BiGRU model: trains variants src/train.py
Experiment orchestration → PSO and NSPSO: obtains tuned parameters src/train.py
PSO and NSPSO → CNN-BiGRU model: evaluates candidates src/pso_nspso.py
PSO and NSPSO → Data preprocessing: uses cross-validation splits src/pso_nspso.py
Experiment orchestration → Root results artifacts: saves predictions src/train.py
Evaluation figures → Root results artifacts: reads prediction and metric files src/evaluate.py
Project crop data → Project preprocessing: loads data crop_yield_project/src/preprocess.py
Project preprocessing → Project results artifacts: writes encoders and scaler crop_yield_project/src/preprocess.py
Project experiments → Project preprocessing: uses preparation crop_yield_project/src/train.py
Project experiments → Project CNN-BiGRU: trains variants crop_yield_project/src/train.py
Project experiments → Project PSO and NSPSO: obtains tuned parameters crop_yield_project/src/train.py
Project PSO and NSPSO → Project CNN-BiGRU: evaluates candidates crop_yield_project/src/pso_nspso.py
Project PSO and NSPSO → Project preprocessing: uses cross-validation splits crop_yield_project/src/pso_nspso.py
Project experiments → Project results artifacts: saves predictions crop_yield_project/src/train.py
Project figures → Project results artifacts: reads prediction and metric files crop_yield_project/src/evaluate.py

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