Being one of the core sectors in the global economy, and sustainable development goals, agriculture is the prime industry that has solid reasons to embrace innovations: by 2050, the global population is predicted to reach 10 billion.

Given such prospects, the importance of agriculture monitoring cannot be overlooked.

As crops grow and ripen, so many things can go wrong: diseases, infestations with pests, or adverse environmental conditions can potentially cause crop failures even before the farmer notices. We identified this as a crucial problem that needs to be addressed and created the EOfactory Monitoring Tool for crop monitoring and providing crop analytics.

EOF Monitoring Product is a smart sensing technology platform that collects metrics about the state of the crops using multiple vegetation indices and this enables the regular monitoring of fields and makes crucial decisions, timely before the conditions go out of hand.

• Manage your fields remotely: Monitor the state of your crops right from the office, learn about the slightest changes on the spot, and make fast and reliable decisions on-field treatment
• Save on costs by up to 30% with the VRA approach: The tool can identify up to 7 zones with different productivity rates and create differential fertilizing, sowing, and irrigation maps, thus saving you a lot of time and resources.
• Full-fledged weather and crop health analytics at your fingertips: Correlate weather with vegetation indices which are the tell-tale signs of how weather conditions influence the health of your crops.

Tools for Field Analytics

Monitoring Indices

Using various indices derived using sentinel imagery, such as NDVI, SAVI, GCI, RECI, EVI2, SIPI, NDRE, and moisture index.

Time-Series Panel

Use Time-Series Panel to Visualize historic vegetation health conditions in your area of interest

Filter and Change the Date Range for the analysis

Weather Forecast

This helps users to get updated with the latest weather information and react to changes in a timely manner. Monitor Wind Speed, Wind Direction, Humidity, Cloud Cover Percentage, and Precipitation Information

Description of the Monitoring Indices

Vegetation indices are an important parameter to develop crop analytics.

Monitor the state of the crops using multiple vegetation indices and moisture indexes.

1. Normalized Difference Vegetation Index (NDVI): NDVI compares the reflectance values of the red and near-infrared regions of the electromagnetic spectrum.
   • The NDVI value, which ranges from -1.0 to 1.0 for each pixel in an image, helps to identify areas of varying levels of plant biomass/vigor. Higher values indicate high biomass/high vigor. 
   • NDVI values give accurate analytics in the middle of the season at the stage of active crop growth.
2. Soil-Adjusted Vegetation Index (SAVI): SAVIaccounts for the differential red and near-infrared extinction through the vegetation canopy. The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths.
   • Empirically derived NDVI products have been shown to be unstable, varying with soil color, soil moisture, and saturation effects from high-density vegetation.
3. Green Chlorophyll Vegetation Index (GCI): GCI is used to estimate the content of leaf chlorophyll in various species of plants. The health of vegetation is directly correlated with the chlorophyll content of the leaves.
   • GCI reflects the physiological state of vegetation; a lower value of GCI represents stressed conditions of plants and a higher value shows the healthy condition of the crop, hence GCI is very useful to measure vegetation health.
4. Red-Edge Chlorophyll Vegetation Index (RECl): The ReCI vegetation index is responsive to chlorophyll content in leaves that is nourished by nitrogen. ReCI shows the photosynthetic activity of the canopy cover.
5. Enhanced Vegetation Index (EVI): The EVI is an ‘optimized’ vegetation index designed to enhance the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences.
6. Structure Intensive Pigment Vegetation Index (SIPI): SIPI index is a reflectance measurement designed to maximize the sensitivity of the index to the ratio of bulk carotenoids (for example, alpha-carotene and beta-carotene) to chlorophyll while decreasing the sensitivity to variation in canopy structure (for example, leaf area index).
   • Increases in SIPI are thought to indicate increased canopy stress (carotenoid pigment). Applications include vegetation health monitoring, plant physiological stress detection, and crop production and yield analysis
7.  Normalized difference red edge index (NDRE): NDRE is an index that can only be formulated when the Red edge band is available in a sensor. It is sensitive to chlorophyll content in leaves (how green a leaf appears), variability in leaf area, and soil background effects. High values of NDRE represent higher levels of leaf chlorophyll content than lower values.
   • Soil typically has the lowest values, unhealthy plants have intermediate values, and healthy plants have the highest values. Consider using NDRE if you are interested in mapping variability in fertilizer requirements or foliar Nitrogen, not necessarily Nitrogen availability in the soil.
8. Normalized Difference Moisture Index (NDMI): The Normalized Difference Moisture Index (NDMI) is sensitive to the moisture levels in vegetation. It is used to monitor droughts as well as monitor fuel levels in fire-prone areas. It uses NIR and SWIR bands to create a ratio designed to mitigate illumination and atmospheric effects.

Understanding the Analytics and Generating Insights

Understanding the Map View

The Map view displays the vegetation health condition for Jan 27, 2021. The green palette represents healthy crop conditions and the red palette represents stressed conditions of the crop.

Analysis shows, that out of the four major circular cropland, three cropland shows healthy crop in the selected area of interest and one region shows stressed cropland condition.

Analysis of Time-Series Bar Chart

• The x-axis shows the dates, the y-axis on left shows the crop acreage value and the y-axis on the right side displays crop health conditions.

• The time period from 2021-01-02 to 2021-02-01 shows the growing phase and healthy crop conditions. From 2021-03-03 shows a dip in the curve and then a stagnant crop growth phase till 2021-04-17. After which the graph shows a sigmoid increase in the crop acreage and crop health condition from 2021-04-27 represents the crop area growing phase and the dip in the curve on 2021-08-05 shows the harvest period of the particular crop within the of interest.