# Wheat Crop Yield Prediction Using Deep LSTM Model

arXiv: https://arxiv.org/abs/2011.01498 · HTML: https://mlsanity.com/paper/2011.01498/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters](https://mlsanity.com/paper/2312.00069/) (2023)
- [Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability](https://mlsanity.com/paper/2212.06864/) (2022)
- [High-Resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production](https://mlsanity.com/paper/2209.12238/) (2022)
- [A Deep Learning Model for Heterogeneous Dataset Analysis -- Application to Winter Wheat Crop Yield Prediction](https://mlsanity.com/paper/2306.11942/) (2023)
- [Retrieval-Augmented Multi-scale Framework for County-Level Crop Yield Prediction Across Large Regions](https://mlsanity.com/paper/2603.07305/) (2026)

## Abstract (arXiv)

An in-season early crop yield forecast before harvest can benefit the farmers to improve the production and enable various agencies to devise plans accordingly. We introduce a reliable and inexpensive method to predict crop yields from publicly available satellite imagery. The proposed method works directly on raw satellite imagery without the need to extract any hand-crafted features or perform dimensionality reduction on the images. The approach implicitly models the relevance of the different steps in the growing season and the various bands in the satellite imagery. We evaluate the proposed approach on tehsil (block) level wheat predictions across several states in India and demonstrate that it outperforms existing methods by over 50%. We also show that incorporating additional contextual information such as the location of farmlands, water bodies, and urban areas helps in improving the yield estimates.