This is the fourth article in our series about sugarcane – a vital cash crop that faces challenges due to climate change. In this article, we look at case studies of top producers and how they monitored and predicted sugarcane with remote sensing and ground level data.
Revolutionizing Sugar Cane Production through Advanced Remote Sensing Capabilities
Ensuring that production meets existing demand is crucial for maintaining food security and market stability. Disease prediction, accurate yield estimation, and real-time weather monitoring empower farmers and industry stakeholders to make informed decisions, mitigate risks, and maximize sustainability.
Platforms like Inference utilize data from satellites, weather, and fields to monitor and optimize crop production with 95%+ accuracy and precision.
Case Study: Thailand Drought 2020 – Using Remote Sensing to Monitor Sugarcane production
In 2020, the 3rd largest producer of sugar suffered severe droughts, which caused a 48% decline in sugarcane yield (from 14.5 million tons in 2019 to 7.5 million tons in 2020/21). These consequences of this significant production decline were:
- Decreased global supply of sugar
- Increased sugar prices
- Sugar stocks dropping
- Negative impact on Thailand’s economy
With the help of remote sensing, stakeholders can predict these events ahead of time and prepare for them. An HSAT client was able to understand how much sugar all of Thailand produced on a field-by-field precision level, with the help of remote sensing technology, custom to their needs.
Case Study: Pakistan Floods 2022 – Using “ground truth” to assess crop damage
In 2022, the 5th largest producer of sugar had catastrophic floods, which caused a 15% decline in sugarcane yield. Traditional satellite data was unable to assess the damage due to thick rain clouds, so an innovative rapid analysis of 2000+ fields was done using radar satellites, drones and field visits.
These insights were provided within 20 days, allowing stakeholders to assess the level of damage and predict recovery measures.