# Development of Crop Yield Estimation Model using Soil and Environmental Parameters

arXiv: https://arxiv.org/abs/2102.05755 · HTML: https://mlsanity.com/paper/2102.05755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Leaf Image-based Plant Disease Identification using Color and Texture Features](https://mlsanity.com/paper/2102.04515/) (2021)

## Abstract (arXiv)

Crop yield is affected by various soil and environmental parameters and can vary significantly. Therefore, a crop yield estimation model which can predict pre-harvest yield is required for food security. The study is conducted on tea forms operating under National Tea Research Institute, Pakistan. The data is recorded on monthly basis for ten years period. The parameters collected are minimum and maximum temperature, humidity, rainfall, PH level of the soil, usage of pesticide and labor expertise. The design of model incorporated all of these parameters and identified the parameters which are most crucial for yield predictions. Feature transformation is performed to obtain better performing model. The designed model is based on an ensemble of neural networks and provided an R-squared of 0.9461 and RMSE of 0.1204 indicating the usability of the proposed model in yield forecasting based on surface and environmental parameters.