# A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction

arXiv: https://arxiv.org/abs/2509.15966 · HTML: https://mlsanity.com/paper/2509.15966/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [CondConv: Conditionally Parameterized Convolutions for Efficient Inference](https://mlsanity.com/paper/1904.04971/) (2019)
- [Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping](https://mlsanity.com/paper/2402.19159/) (2024)
- [MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer](https://mlsanity.com/paper/2309.09067/) (2023)
- [A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction](https://mlsanity.com/paper/2111.08900/) (2021)
- [ImpliHateVid: A Benchmark Dataset and Two-stage Contrastive Learning Framework for Implicit Hate Speech Detection in Videos](https://mlsanity.com/paper/2508.06570/) (2025)
- [SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters](https://mlsanity.com/paper/2312.00069/) (2023)
- [CoTCoNet: An Optimized Coupled Transformer-Convolutional Network with an Adaptive Graph Reconstruction for Leukemia Detection](https://mlsanity.com/paper/2410.08797/) (2024)
- [KisanQRS: A Deep Learning-based Automated Query-Response System for Agricultural Decision-Making](https://mlsanity.com/paper/2411.08883/) (2024)
- [MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for Segmentation of Polyps in Colonoscopy](https://mlsanity.com/paper/2412.19464/) (2024)
- [A Multimodal-Multitask Framework with Cross-modal Relation and Hierarchical Interactive Attention for Semantic Comprehension](https://mlsanity.com/paper/2508.16300/) (2025)
- [An Explainable Deep Neural Network with Frequency-Aware Channel and Spatial Refinement for Flood Prediction in Sustainable Cities](https://mlsanity.com/paper/2509.08003/) (2025)

## Built on by (most cited first, 1 of 1)

- [Uncertainty-Aware Semi-Supervised Framework with Supervised Contrastive Learning for Sugarcane Leaf Disease Detection](https://mlsanity.com/paper/2610.17996/) (2026)

## Abstract (arXiv)

Precise yield prediction is essential for agricultural sustainability and food security. However, climate change complicates accurate yield prediction by affecting major factors such as weather conditions, soil fertility, and farm management systems. Advances in technology have played an essential role in overcoming these challenges by leveraging satellite monitoring and data analysis for precise yield estimation. Current methods rely on spatio-temporal data for predicting crop yield, but they often struggle with multi-spectral data, which is crucial for evaluating crop health and growth patterns. To resolve this challenge, we propose a novel Multi-Temporal Multi-Spectral Yield Prediction Network, MTMS-YieldNet, that integrates spectral data with spatio-temporal information to effectively capture the correlations and dependencies between them. While existing methods that rely on pre-trained models trained on general visual data, MTMS-YieldNet utilizes contrastive learning for feature discrimination during pre-training, focusing on capturing spatial-spectral patterns and spatio-temporal dependencies from remote sensing data. Both quantitative and qualitative assessments highlight the excellence of the proposed MTMS-YieldNet over seven existing state-of-the-art methods. MTMS-YieldNet achieves MAPE scores of 0.336 on Sentinel-1, 0.353 on Landsat-8, and an outstanding 0.331 on Sentinel-2, demonstrating effective yield prediction performance across diverse climatic and seasonal conditions. The outstanding performance of MTMS-YieldNet improves yield predictions and provides valuable insights that can assist farmers in making better decisions, potentially improving crop yields.