frontend

frontend/predict-crop-yield/.gitignore → .gitignore

@@ -1,5 +1,7 @@
 # See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
 
+package-lock.json
+
 # dependencies
 /node_modules
 /.pnp

README.md

@@ -2,31 +2,15 @@
 
 ## Overview
 
-This repository contains a machine learning model designed to predict crop yield for Cassava, Yam, Maize (Corn), Rice, and Sorghum. The model takes into account various independent variables related to agricultural factors.
+This repository contains a machine learning model designed to predict crop yield for Cassava.
 
 ## Dependent Variable
 
 - Crop Yield
 
 ## Independent Variables
 
-1. **Climate and Weather**
-   - Incorporates meteorological data to understand the impact of climate on crop yield.
 
-2. **Soil Quality**
-   - Analyzes soil properties and characteristics that influence crop growth.
-
-3. **Water Management**
-   - Considers the impact of water availability and irrigation practices on crop yield.
-
-4. **Pest and Disease Control**
-   - Evaluates the effectiveness of pest and disease management strategies.
-
-5. **Fertilization**
-   - Takes into account the type and amount of fertilizers used for crop nourishment.
-
-6. **Genetics**
-   - Considers the genetic characteristics of the crops, exploring how different varieties may affect yield.
 
 ## Usage
 
@@ -49,12 +33,9 @@ To utilize the model, follow the steps below:
 
 ## Dataset
 
-The dataset used for training and testing the model is available in the `data` directory. It includes historical information on crop yield and the corresponding independent variables.
 
-## Model Evaluation
 
-The model's performance metrics and evaluation results are documented in the `evaluation.md` file.
+## Model Evaluation
 
-Feel free to explore, contribute, and enhance the capabilities of the crop yield prediction model!
 
 **Note:** Make sure to comply with the licensing terms and conditions outlined in the LICENSE file.