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Smart Roads, Smarter Plantations: Enhancing Agricultural Road Operations with AI and Remote Sensing

Satellite imagery and AI have proven to be game changers in various fields, from weather forecasting to urban planning. In recent years, these technologies have been increasingly used for sustainable agriculture, especially for monitoring crop health and predicting yields. However, their potential applications are not limited to these areas alone. One such application that has gained interest lately is the use of satellite imagery and AI for oil palm plantation road monitoring.

Oil palm plantation roads are crucial for the transportation of workers, equipment, and harvested crops. However, they are also susceptible to damage caused by heavy vehicles and water, particularly during the wet season. Damage to these roads can cause disruptions to harvesting schedules and associated losses in revenue, as well as being a significant cost center in themselves. As road conditions are constantly evolving, many plantations resort to reactive measures such as gravel replenishment, which is expensive, unmonitored and short-term. Therefore, it is essential to gain a better understanding of plantation-wide road conditions, and develop a preventative maintenance strategy to prolong road lifespan and reduce long-term expenses.

A large proportion of plantation roads are unpaved plantation roads, which are particularly prone to erosion and degradation. Photo credit: Kelly from Pexels

Adatos Road Analysis Tool

At Adatos, we are developing a road analysis tool that combines established road engineering principles with our expertise in satellite imagery and AI. The tool is designed to forecast road damage risk and flood areas, as well as provide recommendations for stone purchases and identify historical hotspots. With the output maps generated by the tool, end-users can have a holistic and detailed view of road conditions, enabling them to plan preemptively and ensure efficient operations year-round. Our analysis is tailored to specific plantation operations, such as usage of road classification systems.

1. Damage Risk

One of the components of the road analysis tool is the assessment of damage risk by accounting for soil moisture, flood risk, erosion risk, and rainfall forecast. Soil moisture is a crucial factor in road maintenance as it affects the stability of the road base. Satellites equipped with active microwave sensors are particularly sensitive to soil moisture content, which can be used to determine road condition. Flood risk can also be assessed by analysing the hydrology of the area to identify areas prone to flooding. Erosion risk can be determined by analysing the topography of the area and identifying areas with high slope angles. Finally, the Adatos rainfall forecast model is used to predict the likelihood of heavy rain and potential damage to the road.

2. Flood Area Forecasting

Whilst damage risk gives a relative score for plantation roads, flood area forecasting combines terrain, flood risk, historical flood data and rainfall data to predict areas that are likely to experience flooding during the rainy season. Flood data is used to provide information on the extent and duration of past floods and to validate the forecasts. This information can be used to plan alternative routes for transportation and to implement preventative measures such as the construction of drainage systems, which can reduce the risk of flood damage to plantation roads.

3. Stone Purchase Recommendations

Whilst the ultimate goal is to enable long-term planning, if stone purchase records are available, we are also able to provide AI-driven recommendations for stone replenishment in the short-term. By finding the relationship between road conditions, terrain, hydrology and historical management records, our models can suggest quantities of gravel that are required for specific areas. These suggestions help managers to strategically allocate resources and provide more accurate cost estimates.

4. Historical Analysis

In addition to assessing current road conditions and predicting potential risks, historical analysis is a key component of our road analysis tool. By analysing historical data on road conditions, maintenance schedules, and weather patterns, we can identify hotspots where damage to roads and associated costs have been particularly high. This information can be used to develop targeted preventative maintenance plans for these areas, reducing the risk of damage and associated costs in the future. Furthermore, historical analysis can help identify trends and patterns that may inform decision-making, such as changes in weather patterns or the effectiveness of past maintenance strategies.

Hitting the Road

In sum, the use of satellite imagery and AI for oil palm plantation road monitoring has significant potential to increase the efficiency of operations in wet seasons, prolong road lifespan, and reduce long-term expenses. Adatos' road analysis tool offers a comprehensive solution to road maintenance, combining satellite and AI insights to forecast road damage risk and flood areas, provide stone purchase recommendations, and identify historical hotspots. This tool can provide a holistic and detailed view of road conditions, enabling targeted preventative maintenance plans.

If you are interested in implementing a proactive and data-driven approach to road maintenance and management, please reach out to us at Adatos. Our team can work with you to build a customised roadmap towards smarter roads.


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