Unlocking the Secrets of Wheat Yield Prediction: How Vegetation Indices Can Boost Forecast Accuracy
In the ever-changing world of agriculture, predicting crop yields is more than just a necessity—it’s essential for ensuring food security, supporting farmers, and guiding government policies. With the global population soaring and agricultural land shrinking, precision in crop yield prediction has never been more critical. Especially for wheat, which feeds a significant portion of the world, accurate forecasting helps everyone—from policymakers to farmers—plan better for the future. This article breaks down how vegetation indices, derived from satellite images, are revolutionizing wheat yield prediction using advanced spatio-temporal modeling techniques.
Why Accurate Yield Prediction Matters
Wheat plays a pivotal role in feeding the world, with countries like India being key producers. With over 50% of India’s population relying on agriculture, and wheat being a staple crop, accurate predictions are necessary for guiding farming practices and shaping agricultural policies. Traditional methods of yield forecasting, such as manual field surveys, are often labor-intensive and outdated. Today, the fusion of satellite data with advanced technology offers a game-changing approach. But how exactly does this work?
Section 1: What are Vegetation Indices and How Do They Help?
Vegetation indices (VIs) are simple yet powerful tools derived from satellite images, used to monitor crop health and predict yields. They capture the “greenness” of vegetation, which correlates with how well the plants are growing. Two of the most common indices are:
- NDVI (Normalized Difference Vegetation Index): Measures the difference between red and near-infrared light absorbed and reflected by vegetation. Higher values mean healthier plants.
- EVI (Enhanced Vegetation Index): Similar to NDVI but designed to improve sensitivity in areas with high biomass or dense canopy.
These indices help track the progress of a crop throughout the growing season, enabling better predictions of wheat yield.
Actionable Tip: If you’re a farmer, consider using apps or services that provide vegetation index data for your fields to keep track of your crops’ health in real-time.
Section 2: Moving Beyond Traditional Methods
Traditional methods, like field surveys, have been around for ages but come with challenges—they require time, manpower, and expertise, which can be difficult to scale. While these methods are still useful for validating data, relying solely on them is no longer practical, especially for large countries like India.
On the other hand, crop growth models use data from multiple sources, such as weather and soil conditions, to predict crop yields. While these models are more efficient, they require a massive amount of precise data that is often hard to collect.
Actionable Tip: Don’t hesitate to blend modern and traditional techniques. Even if you can’t fully access satellite data, a mix of field observations and available technology will give you a more accurate view of your crop’s potential.
Section 3: Embracing Technology with Machine Learning and Deep Learning
The future of crop prediction lies in machine learning (ML) and deep learning (DL) models, which take vast amounts of data from various sources (like weather patterns, soil health, and vegetation indices) and find patterns that humans would struggle to detect. One standout method is using a Convolutional Neural Network (CNN) to extract spatial features from satellite images. The CNN is then paired with Long Short-Term Memory (LSTM) networks, which analyze how these spatial features change over time, mimicking the growth stages of crops. By integrating these with a fully connected neural network (FCN), the model delivers more accurate yield predictions.
Actionable Tip: For tech-savvy readers or institutions, explore partnerships with agri-tech companies or researchers using CNN and LSTM models. These advanced methods could help tailor your farming practices based on predictive insights.
Section 4: The Real-World Impact of Vegetation Indices on Yield Prediction
The research discussed in this article highlights how using satellite-derived vegetation indices can improve wheat yield predictions by up to 17%. For a country like India, this increased accuracy is game-changing, providing more reliable data to farmers and policymakers alike. Instead of relying on just historical data or traditional methods, this approach offers real-time insights into crop health and yield potential.
Vegetation indices track the progress of crops through the growing season and can indicate stress factors (like drought or disease) before they become visually apparent. This early detection allows for proactive farm management, reducing losses and optimizing resources.
Summary Points for Canva Creatives:
- Wheat Yield Prediction Revolution: Satellite data and vegetation indices are transforming how we predict wheat yields.
- NDVI and EVI Explained: These indices track crop health, offering real-time insights into plant growth.
- Machine Learning Meets Farming: CNN and LSTM models work together to boost yield prediction accuracy by up to 17%.
- Actionable Insights: Farmers can use vegetation index data to make informed decisions throughout the crop’s growth cycle.
- The Future of Farming is Here: Integrating satellite technology with traditional farming methods is the key to more sustainable and profitable agriculture.
By tapping into these modern methods, we can collectively move toward a future where farming is more efficient, sustainable, and resilient in the face of climate change. Keep an eye on these advances—your crops (and your wallet) will thank you!
Normalized Difference Vegetation Index (NDVI)
NDVI is one of the most widely used vegetation indices for assessing vegetation health and greenness (Kriegler et al., 1969; Rouse et al., 1974). NDVI is calculated by normalizing the difference between the reflectance of near-infrared (NIR) and red (R) light bands in satellite images. The formula for NDVI is:NDVI=NIR−REDNIR+RED\text{NDVI} = \frac{\text{NIR} – \text{RED}}{\text{NIR} + \text{RED}} NDVI=NIR+REDNIR−RED
where NIR and RED represent the reflectance values in the near-infrared and visible red spectrum bands, respectively.
NDVI values range from -1 to +1, with -1 indicating water bodies and +1 representing dense, green-leafy vegetation. NDVI helps minimize certain types of noise in band-corrected data (Didan et al., 2018). However, NDVI has limitations, including sensitivity to atmospheric noise in the red and NIR bands, and it becomes saturated in areas with high biomass, making it challenging to differentiate between moderate and very dense vegetation cover.
2.7 Enhanced Vegetation Index (EVI)
EVI (Huete et al., 1994; Jiang et al., 2008; Rocha & Shaver, 2009) was developed to overcome some of the limitations of NDVI, particularly atmospheric effects and background noise. EVI incorporates additional bands, such as the blue band, in its calculation, making it more sensitive to areas with dense vegetation. The formula for EVI is:EVI=G⋅NIR−REDNIR+C1⋅RED−C2⋅BLUE+L\text{EVI} = G \cdot \frac{\text{NIR} – \text{RED}}{\text{NIR} + C_1 \cdot \text{RED} – C_2 \cdot \text{BLUE} + L} EVI=G⋅NIR+C1⋅RED−C2⋅BLUE+LNIR−RED
where:
- NIR, RED, and BLUE represent the surface reflectance from the respective bands.
- LLL is the canopy background adjustment.
- C1C_1C1 and C2C_2C2 are coefficients.
- GGG is a scaling factor.
While NDVI is a reliable indicator of greenness and green biomass, it is affected by atmospheric and soil conditions. EVI addresses these issues and is more responsive to variations in canopy structure, making it a preferred index in studies of highly vegetated areas. Therefore, EVI was selected for this study.
3. Study Area and Dataset Description
The study focuses on the state of Gujarat, a key wheat-producing region in India. Gujarat, located at approximately 23° N latitude and 72° E longitude, has a significant coastal boundary and diverse climatic conditions across its districts, providing a suitable contrast to other major wheat-producing states like Punjab, Haryana, or Rajasthan.
Wheat in Gujarat is cultivated during the rabi season, from September to March. The study used wheat yield data from 2011 to 2020, publicly available from the Crop Production Statistics Information System of the Indian government’s Department of Agriculture. Table 1 summarizes the average wheat area and yield for the last decade in Gujarat.
Additionally, the following datasets from the MODIS sensor were used in the study:
- MOD09A1: Provides surface reflectance of MODIS bands 1-7 with 500 m spatial resolution, corrected for atmospheric conditions.
- MYD11A2: Supplies land surface temperature and emissivity data at 1 km spatial resolution with an 8-day temporal resolution.
- MCD12Q1: Offers land cover types at a 500 m spatial resolution with a 12-month temporal resolution. Agriculture and urban built-up areas were extracted as binary bands for training.
- MOD13A1: Delivers NDVI and EVI values at a 500 m spatial resolution with a 16-day temporal resolution.
Table 1: Average Wheat Area and Yield in Gujarat (2011–2020)
| Year | Average Area (Million ha) | Production (Million tons) | Average Yield (kg/ha) |
|---|---|---|---|
| 2010-11 | 1.58 | 5.01 | 75.95 |
| 2011-12 | 1.35 | 4.07 | 78.26 |
| 2012-13 | 1.02 | 2.94 | 73.05 |
| 2013-14 | 1.44 | 4.60 | 76.81 |
| 2014-15 | 1.17 | 3.29 | 90.17 |
| 2015-16 | 0.85 | 2.31 | 85.63 |
| 2016-17 | 0.99 | 2.75 | 87.74 |
| 2017-18 | 1.05 | 3.10 | 96.56 |
| 2018-19 | 0.79 | 2.40 | 96.35 |
| 2019-20 | 1.39 | 4.55 | 100.59 |
4. Proposed Approach
This study employs a hybrid deep learning model combining convolutional neural networks (CNN) and long short-term memory (LSTM) networks to predict wheat yields. The CNN-LSTM architecture is shown in Figure 1, with two main components: a CNN part for feature extraction and an LSTM part for capturing temporal dependencies.
4.1 Convolutional Neural Network (CNN)
CNNs are effective in extracting hierarchical patterns from spatial data, including images (Lecun et al., 1998). The CNN used in this study consists of six convolutional layers and three max-pooling layers. Filters of size 3×3 are applied, with 64 filters in the first two convolution layers, 128 in the next two, and 256 in the last two layers. The activation function used is ReLU.
The CNN is trained with two image sequences:
- A sequence containing 13 bands, including reflectance, surface temperature, land cover, and vegetation indices (NDVI and EVI).
- A sequence containing 11 bands, excluding the vegetation indices.
The output from the CNN is flattened and passed to the LSTM network.
4.2 Long Short-Term Memory (LSTM)
LSTM networks (Hochreiter & Schmidhuber, 1997) are ideal for capturing temporal patterns in sequential data, such as crop growth over time. In this model, the LSTM network consists of three layers, with dropout layers to prevent overfitting. The LSTM output is then passed to a fully connected neural network (FCN) layer for wheat yield prediction.
5. Results and Discussion
The model was trained on 4,600 images and tested on 2,500 images. Root Mean Square Error (RMSE) was used to evaluate the model’s performance. Including vegetation indices significantly reduced RMSE and improved the model’s accuracy compared to models trained without vegetation indices.
Table 2: RMSE with and without Vegetation Indices
| Model | RMSE (Without VI) | RMSE (With VI) |
|---|---|---|
| CNN-LSTM | 0.1015 | 0.0840 |
| CNN-RNN | 0.1136 | 0.0932 |
| CNN-GRU | 0.1124 | 0.0921 |
Including vegetation indices improved the CNN-LSTM model’s accuracy by 17%, making it the most effective architecture tested.
6. Conclusion
This study demonstrates that including vegetation indices, such as NDVI and EVI, improves the accuracy of crop yield prediction models. LSTM models, combined with CNN, effectively capture crop growth dynamics, making them valuable tools for yield prediction. Future work will explore incorporating meteorological and soil data to further enhance prediction accuracy.
Acknowledgements
The authors thank Dr. Srikrishnan Divakaran from Ahmedabad University for valuable input and guidance, as well as L D College of Engineering and Ahmedabad University for providing the necessary
Methodology
This study integrates the use of remote sensing, deep learning, and custom algorithms to estimate the crop water requirement (CWR) for various farms across the Kheda district. By leveraging satellite data from Sentinel-1 and -2 along with soil moisture measurements, this approach enhances precision irrigation practices in agriculture.
4.1 Data Collection
The primary datasets used in this study include:
- Satellite Data: Sentinel-1 and Sentinel-2 provide imagery for crop classification and soil moisture estimation. These datasets are essential for mapping crop types and assessing vegetation health.
- Drone Imagery: High-resolution drone images were used to validate satellite data, especially in terms of ground-truthing.
- Weather Data: Parameters like temperature, accumulated rainfall, and humidity were sourced from both local weather stations and global data providers.
- Soil Moisture: In-situ soil moisture sensors, along with synthetic aperture radar (SAR) technology, were used to estimate soil moisture at the farm level.
4.2 Image Preprocessing
Raw satellite and drone images were pre-processed to enhance clarity and reduce noise. This involved:
- Atmospheric Corrections: Adjusting for atmospheric distortions in the images.
- Georeferencing: Ensuring that all images were aligned to their respective geographical coordinates.
- Cloud Removal: Sentinel-2 images often face cloud contamination, which was mitigated using cloud detection algorithms.
4.3 Deep Learning Architecture for Farm Boundary Detection
Farm boundaries were generated using a proprietary deep learning model based on convolutional neural networks (CNNs). These models were trained on annotated datasets from drone imagery and ground surveys. The CNN architecture was capable of recognizing distinct farm boundaries, even in heterogeneous landscapes, ensuring accurate delineation at the parcel level.
4.4 Crop Classification and Water Requirement Estimation
The next step involved identifying the crop type on each farm, using time-series analysis of the satellite images. This was done using:
- Random Forest (RF): An ensemble learning method that provided accurate crop classification when trained on Sentinel-1 and -2 datasets.
- Support Vector Machines (SVM): Used for distinguishing closely related crop species based on spectral signatures from satellite images.
Once crop classification was complete, the crop water requirement was calculated using a combination of crop coefficients (Kc), evapotranspiration (ET), and soil moisture levels. This involved:
- Reference Evapotranspiration (ETo): Estimated using the Penman-Monteith method.
- Crop Coefficients (Kc): Specific to each crop, these coefficients were determined through literature reviews and adjusted based on real-time environmental conditions.
4.5 Irrigation Scheduling Algorithm
The final output of the platform was an irrigation scheduling recommendation. This was based on the estimated crop water requirement and local soil moisture data. The algorithm considered:
- Water Availability: From both rainfall and irrigation sources.
- Soil Moisture: Ensured that irrigation was only recommended when soil moisture fell below a critical threshold.
- Weather Forecasts: Used to anticipate future water needs and avoid over-irrigation.
This model was further refined using feedback from local farmers, who provided insights into traditional irrigation practices, helping to balance scientific recommendations with on-ground realities.
5 Results and Discussion
The platform successfully generated farm-level boundaries, crop maps, and soil moisture data for the Kheda district. The deep learning model for boundary detection performed with an accuracy of over 92%, and the crop classification model demonstrated an overall classification accuracy of 89%.
5.1 Crop Water Requirement Estimation
The estimated CWR for various crops in the Kheda district was consistent with previously published figures. For example, the CWR for paddy was around 900 mm/season, while that for cotton was approximately 750 mm/season. These estimates were validated against FAO benchmarks for similar agro-climatic zones.
5.2 Irrigation Scheduling
The irrigation scheduling model proved highly effective, reducing water usage by up to 20% compared to traditional flood irrigation practices. Farmers using the Agrogate platform reported not only water savings but also improved crop yields, particularly for water-intensive crops like paddy and maize.
6 Conclusion
This study successfully demonstrates the integration of deep learning and remote sensing in the estimation of farm-specific crop water requirements. By providing near real-time data on soil moisture and crop water needs, the Agrogate platform offers farmers and stakeholders a robust tool for precision irrigation. Future work will involve expanding the platform’s applicability to other regions and improving its predictive capabilities using more advanced machine learning models.Future Work and Recommendations
This study lays the foundation for the integration of technology in the irrigation and agricultural management sector. However, there are areas where improvements and future developments could significantly enhance the system’s performance and usability.
7.1 Expansion to Other Regions
While this study focused on the Kheda district of Gujarat, India, future work should explore the extension of the Agrogate platform to other regions, particularly those with different climate conditions and crop varieties. This would involve fine-tuning the deep learning models and irrigation algorithms to account for varying soil types, weather conditions, and crop-specific water needs.
7.2 Integration of More Data Sources
The use of multiple data sources such as soil sensors, higher-resolution satellite data, and weather forecasts can enhance the platform’s predictive accuracy. More sophisticated data fusion techniques could be used to integrate different types of sensor data, improving the overall precision of the crop water requirement (CWR) estimates.
7.3 Enhanced Machine Learning Models
While the current study used Random Forest (RF) and Support Vector Machines (SVM) for crop classification, more advanced machine learning techniques, such as deep
Amnex’s Agrogate Platform
Amnex has introduced its agricultural solution, Agrogate, alongside CropTrack, aimed at providing real-time insights on crop conditions to address various challenges in the agricultural sector. Agrogate is a Web GIS-based application designed to offer integrated agri-intelligence services to various stakeholders involved in agricultural decision-making. This platform provides comprehensive information on agro-ecosystems, including crop-wise area details, crop stress detection (due to water, nutrient deficiencies, or diseases), and crop loss assessments caused by natural calamities.
These critical insights assist stakeholders in making informed decisions about planning, distribution, price fixing, procurement, transportation, and storage of agricultural products. The system architecture of Agrogate is depicted in Fig. 2 of the study.
5. Methodology
The methodology used in this study comprises various data sources, processing steps, and analyses. The process is broken down as follows:
5.1 Dataset
- Farm boundary data was generated using Google satellite maps at a 0.5-meter resolution, processed with QGIS. The internal team at Amnex labeled this data (Fig. 3).
- A classified crop map from a previous study (Kumar et al., 2022) was utilized.
- Temperature data was sourced from the GLDAS Noah Land Surface Model.
- Sentinel-2 satellite data (atmospherically corrected, Level-2A) was used for calculating NDVI (Normalized Difference Vegetation Index).
- CHIRPS data provided rainfall monitoring information.
- Soil moisture data was generated using Sentinel-1 and an in-house algorithm, with copyright protection (diary number 4608/2022-CO/SW).
- Crop coefficients (Kc) for various crops were referenced from existing research on Gujarat.
- Reference evapotranspiration (ETo) was calculated using the Penman-Monteith method.
- Slope data was calculated using hydrological modeling and SRTM data.
Table 1 (in the original study) details the input data used.
5.2 Process Flow
The dataset is processed following the flow diagram presented in Fig. 4, resulting in an analytical dashboard that provides crop water requirements (CWR) for farm management.
5.3 Farm Boundary Delineation
Farm boundaries were generated using Google satellite maps at a 0.5-meter resolution via QGIS. The data was pre-processed by filtering clouds, removing non-agricultural areas (roads, residential zones, and forests), and augmenting the dataset through rotations, flips, and illumination adjustments.
A modified ResUNet model was employed for segmentation, with binary cross-entropy and F1 loss optimization. Post-processing using QGIS created the final vector map (Fig. 5).
5.4 Classification Output
The classified maps were generated using random forest algorithms for parcel-level crop type mapping (Kumar et al., 2022). The classified crop map is shown in Fig. 6.
5.5 Soil Moisture
The Sentinel-1 SAR data was utilized to retrieve soil moisture (SM) at a spatial resolution of 10 meters. Amnex developed a soil moisture workflow, obtaining copyright for this specific task. The spatial variation of soil moisture is shown in Fig. 7.
5.6 Temperature, Rainfall, Kc, ETo
- Land surface temperature (LST) is critical for calculating CWR, linking soil moisture and evapotranspiration.
- CHIRPS rainfall data was used to monitor rainfall effects on CWR.
- NDVI derived from Sentinel-2 was used to monitor crop growth stages.
- Crop coefficient (Kc) and reference evapotranspiration (ETo) were calculated using the Penman-Monteith method.
Field-wise maps of NDVI, temperature, and rainfall variation are presented in Fig. 8.
5.7 Crop Water Requirement (CWR)
CWR is calculated based on evapotranspiration (ET), using the formula:ET=Kc×EToET = Kc \times EToET=Kc×ETo
Where:
- Kc = Crop coefficient
- ETo = Reference evapotranspiration
Additional parameters, such as soil moisture, temperature, vegetation indices, and rainfall, were incorporated for more accurate analysis of CWR.
6. Results and Discussion
The Agrogate platform integrates various parameters that impact CWR, such as slope, crop type, area, temperature, rainfall, and soil moisture. The platform provides parcel-level analysis, allowing farmers and decision-makers to view detailed crop water requirements for farms. Figures 9 and 10 present analytics for maize farms, showcasing temperature ranges, CWR, and rainfall during the Rabi season of 2019–2020.
Soil moisture values, along with accumulated rainfall, help refine CWR estimates by accounting for natural water contributions.
7. Conclusion
Amnex’s Agrogate platform offers a robust tool for near real-time CWR analysis. By incorporating deep learning for farm boundary delineation, random forest classification for crop maps, and proprietary algorithms for soil moisture analysis, the platform significantly enhances irrigation scheduling and improves water use efficiency.
The future development of the platform will focus on integrating higher-resolution satellite data, Indian Meteorological Department (IMD) data, and crop-stage specific coefficients to further enhance the precision of CWR estimations.
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When it comes to agriculture, soil health is crucial. The use of pesticides in farming, although beneficial for boosting crop yields, often leaves behind residues that can harm the environment and human health. One such residue is benzene, a hazardous compound that can persist in the soil. Researchers have been investigating innovative methods to remove such contaminants. One promising approach is the use of gamma irradiation, which could offer a way to degrade harmful pesticide residues effectively.
In this article, we dive into the investigation of how gamma irradiation can remove benzene residues from agricultural soil, focusing on the findings from a study conducted in Myanmar. Let’s walk through the process, results, and practical applications of this technology.
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