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Potato Analyses

Overview

The Vultus Analysis Platform provides four unique analyses for potato growers:

  1. Yield prediction
  2. Starch prediction
  3. Disease prediction
  4. Leaf nitrogen content

image

Yield and Starch Prediction

Yield and starch level predictions offer invaluable benefits to potato farmers, serving as powerful tools to optimize crop management and maximize profitability.

Improved Planning: Predictions of yield and starch levels allow growers to plan operations more effectively. By knowing what to expect in terms of yield, growers can better allocate resources such as labor, fertilizers, and pesticides. They can also make informed decisions about storage and transportation logistics based on projected harvest volumes.

Optimized Input Use: With accurate predictions, growers can tailor their input use to match the expected yield and starch levels. This means applying the right amount of fertilizers and irrigation to optimize crop growth and quality while minimizing waste. By avoiding over-application of inputs, growers can reduce costs and minimize environmental impact.

Enhanced Crop Quality: Starch level predictions are particularly valuable for potato growers, as starch content significantly influences the quality and marketability of the potatoes. By anticipating starch levels, growers can harvest potatoes at the optimal time to meet market specifications. This ensures that the potatoes have the desired texture, cooking characteristics, and suitability for processing into various products such as chips, fries, and starch.

Market Access and Pricing: Accurate yield and starch level predictions can give growers a competitive edge in the market. By consistently delivering high-quality potatoes with predictable characteristics, growers can attract premium buyers and negotiate better prices. Additionally, having reliable predictions allows growers to plan their marketing strategies more effectively, ensuring that they meet the demands of their customers and capitalize on market opportunities.

Risk Management: Yield and starch level predictions also help growers manage risk more effectively. By having a clear understanding of what to expect from their crop, growers can make contingency plans in case of unexpected events such as adverse weather conditions, pest outbreaks, or market fluctuations. This allows them to mitigate potential losses and maintain a stable income stream.

Disease Risk Prediction

Disease risk prediction empowers potato growers to make informed decisions, implement targeted interventions, and effectively manage disease threats throughout the growing season. By leveraging these predictive tools, growers can enhance crop health, optimize resource use, and ultimately improve the sustainability and profitability of their operations.

The Vultus Analysis Platform provides disease risk predictions for three common threats to potato crops.

Early Blight

Early blight tends to strike early in the growing season, hence its name. It's caused by a fungus called Alternaria solani, which can find its way into fields during warm, humid weather.

Early blight can be found in the soil and on infected plant debris, waiting for the perfect opportunity to strike. When conditions are right, the fungus unleashes its attack. It starts by creating small, dark lesions on the lower leaves of our potato plants, which may initially resemble small, circular spots with dark borders.

As the disease progresses, these lesions grow larger and spread, eventually causing the leaves to turn yellow and wither. In severe cases, early blight can even affect the stems and tubers of potato plants, leading to reduced yields and poor-quality potatoes.

It is important to apply fungicides before symptoms appear, especially during periods of warm, humid weather when the risk of infection is highest. The Vultus Platform can provide accurate predictions of this risk, allowing growers to minimize early blight impact and safeguard the potato harvest.

Late Blight

Late blight is caused by a microorganism known as Phytophthora infestans, which thrives in cool, moist environments. When potato fields get a bit too damp, especially in cooler weather, this organism can spread rapidly.

Late blight can affect any potato variety. It starts with small, water-soaked spots on the leaves, often appearing oily and dark. These spots quickly grow into larger lesions, turning the leaves yellow, then brown, and eventually causing them to wither and die. The blight doesn't stop there—it can also attack the potato tubers themselves, turning them into mushy, foul-smelling messes unfit for consumption or sale.

Late blight spreads very quickly. The Vultus Platform can provide accurate predictions of this risk, allowing growers to minimize the impact of late blight and protect theie potato harvest.

Aphid Risk

Aphids are tiny insects that seem harmless at first glance, but they can wreak havoc on your potato plants if left unchecked. They have a voracious appetite for the sap of potato plants, using their piercing mouth parts to suck out vital nutrients. This feeding damage weakens the plants, stunting their growth and reducing yields.

Aphids also transmit harmful plant viruses as they feed. One of the most notorious viruses spread by aphids is Potato Virus Y (PVY), which can cause significant yield losses and reduce the quality of your potatoes.

The risk of aphid infestation is highest during periods of warm weather when populations can explode rapidly. It's essential to keep a close eye on your potato fields, monitoring for signs of aphid activity such as clusters of small, soft-bodied insects on the undersides of leaves. Yellowing or curling leaves can also indicate aphid feeding damage.

The Vultus Aphid Risk analysis provides an early warning that can mitigate the risk of aphids damaging your potato crop.

Leaf Nitrogen Content

Leaf nitrogen content refers to the amount of nitrogen present in the leaves of a plant. Nitrogen is an essential nutrient for plant growth and development, playing a crucial role in processes such as photosynthesis, protein synthesis, and overall plant metabolism. This unique Vultus service is intended for use in the early growth stages of the potato crop.

Knowing the leaf nitrogen content allows growers to better:

  1. Optimize fertilization: Monitoring leaf nitrogen content helps farmers determine the optimal amount of nitrogen fertilizer to apply to their crops. By ensuring that plants have an adequate supply of nitrogen, farmers can maximize crop growth and yield potential while minimizing input costs and environmental impacts.
  2. Diagnose nutrient deficiencies: Insufficient nitrogen levels in plant tissues can lead to nutrient deficiencies. Regular assessment of leaf nitrogen content allows growers to detect nutrient deficiencies early and take corrective measures, such as adjusting fertilizer application rates or using nitrogen-containing supplements.
  3. Balance nutrient management: Excessive nitrogen application can lead to environmental pollution, nutrient runoff, and negative impacts on soil and water quality. Monitoring leaf nitrogen content helps growers strike a balance between providing adequate nitrogen for crop growth and avoiding over-fertilization, thus promoting sustainable agricultural practices.
  4. Maximize crop yield and quality: By optimizing leaf nitrogen content, growers can promote vigorous plant growth, increase biomass production, and enhance the nutritional value of their potato crops.
  5. Respond to environmental conditions: Leaf nitrogen content can vary depending on environmental factors such as soil fertility, temperature, moisture levels, and pest pressure. Monitoring leaf nitrogen content helps growers adapt their management practices to changing environmental conditions.

Processing Yield/Starch Images

You can use the processPotatoAnalysis() mutation to process the yield/starch for a given sowing date and expected harvesting date.

Usage

The processPotatoAnalysis() mutation is used to process potato analysis images based on the satellite data. The endpoint requires four arguments:

  • polygonId*: The polygonId is the unique ID generated when you register/create a polygon in the API. You use this unique identifier to specify the plot you want to analyse.
  • sowingDate*: The sowingDate is the date that seeds went into the ground.
  • expectedHarvestingDate*: The expectedHarvestingDate is the expected date the potatoes can be harvested.
  • analysisType*: The type of potato analysis to be requested. Can either be of type Starch or Yield.

The endpoint only returns the status of the request itself, with the requestId. The request can be tracked using the requestId using the retrieveRequestDetails() endpoint. For more information on monitoring requests, please refer to our monitoring documentation.

Parameter Constraints

  1. Expected harvesting date can only be a date after the sowing date.
  2. The sowing date has to be a date in the past.
  3. The sowing date can only be a date from 2023-01-01. An earlier date is not allowed.
  4. The difference in time between the sowing date and the expected harvesting date has to be minimum 3 months, and maximum 9 months.

Example

mutation {
  processPotatoAnalysis(
    polygonId:                "PolygonId"
    sowingDate:               "2024-04-01"
    expectedHarvestingDate:   "2024-10-01"
    analysisType:             "Starch"
  )
  {
    IsSuccess
    Message
    Status
    Result {
      requestId
    }
  }
}

Retrieving Yield/Starch Images

You can use the retrievePotatoAnalysis() query to retrieve the yield/starch for a given sowing date and expected harvesting date.

Usage

The retrievePotatoAnalysis() query is used to retrieve potato analysis images based on the satellite data. The endpoint requires two arguments:

  • polygonId*: The polygonId is the unique ID generated when you register/create a polygon in the API. You use this unique identifier to specify the plot you want to analyse.
  • sowingDate: The sowingDate is the date that seeds went into the ground. When specifying the sowing date, the expected harvesting date also has to be provided.
  • expectedHarvestingDate: The expectedHarvestingDate is the expected date the potatoes can be harvested. When specifying the expected harvesting date, the sowing date also has to be provided.
  • analysisType*: The type of potato analysis to be requested. Can either be of type Starch or Yield.
  • processingHistory: This optional parameter allows for retrieving the processing history of yield/starch analysis images over time. If true, returns the history of processed images over time. If false or undefined, returns only the latest image.

The endpoint only returns the status of the request itself, with the requestId. The request can be tracked using the requestId using the retrieveRequestDetails() endpoint. For more information on monitoring requests, please refer to our monitoring documentation.

Parameter Constraints

  1. Expected harvesting date can only be a date after the sowing date.
  2. The sowing date has to be a date in the past.
  3. The sowing date can only be a date from 2023-01-01. An earlier date is not allowed.
  4. The difference in time between the sowing date and the expected harvesting date has to be minimum 3 months, and maximum 9 months.

Example

query {
  retrievePotatoAnalysis(
    polygonId:                "PolygonId"
    sowingDate:               "2024-04-01"
    expectedHarvestingDate:   "2024-10-01"
    analysisType:             "Starch"
    processingHistory:        true
  )
  {
    IsSuccess 
    Message 
    Status 
    Result { 
      colorlegend 
      png 
      tif 
      json   
    }
  }
}

Retrieving Disease Predictions

You can use the retrieveDiseasePrediction() query to retrieve the disease predictions for aphid, early blight, and late blight.

Usage

The retrieveDiseasePrediction() query is used to retrieve disease predictions. The endpoint requires two arguments:

  • polygonId*: The polygonId is the unique ID generated when you register/create a polygon in the API. You use this unique identifier to specify the plot you want to analyse.
  • diseaseType*: The type of disease for which you want a prediction. Must be either of value Aphid, EarlyBlight or LateBlight.

Example

query { 
  retrieveDiseasePrediction( 
    polygonId:      "PolygonId"
    diseaseType:    "LateBlight"
  ) 
  { 
    IsSuccess 
    Message 
    Status 
    Result { 
      png 
      predictions{ 
        dateTime     
        humidity     
        precipitation    
        risk     
        sprayInterval    
        temperature  
      }  
    } 
  } 
}

Processing Leaf Nitrogen Images

You can use the processLeafNitrogen() mutation to process leaf nitrogen images. Leaf Nitrogen is a unique service developed by Vultus. The service provides an assessment of the amount of nitrogen (ppm) available in the leaf of a potato plant.

Usage

The processLeafNitrogen() mutation is used to process leaf nitrogen images based on the satellite data. The endpoint requires three arguments:

  • polygonId*: The polygonId is the unique ID generated when you register/create a polygon in the API. You use this unique identifier to specify the plot you want to analyse.
  • startDate*: This date specifies the start of the date range that you want to process.
  • endDate*: This date specifies the end of the date range that you want to process.

The endpoint only returns the status of the request itself, with the requestId. The request can be tracked using the requestId using the retrieveRequestDetails() endpoint. For more information on monitoring requests, please refer to our monitoring documentation.

Example

mutation {
  processLeafNitrogen(
    polygonId:         "PolygonId"
    startDate:         "2024-01-01"
    endDate:           "2024-05-01"
  )
  {
    IsSuccess
    Message
    Status
    Result {
      requestId
    }
  }
}

Retrieving Leaf Nitrogen Images

You can use theretrieveLeafNitrogen() query to retrieve the leaf nitrogen images. Leaf Nitrogen is a unique service developed by Vultus. The service provides an assessment of the amount of nitrogen (ppm) available in the leaf of a potato plant.

Usage

The retrieveLeafNitrogen() query is used to retrieve leaf nitrogen images. The endpoint requires three arguments:

  • polygonId*: The polygonId is the unique ID generated when you register/create a polygon in the API. You use this unique identifier to specify the plot you want to analyse.
  • startDate*: This date specifies the start of the date range that you want to retrieve.
  • endDate*: This date specifies the end of the date range that you want to retrieve.

Example

mutation {
  retrieveLeafNitrogen(
    polygonId:         "PolygonId"
    startDate:         "2024-01-01"
    endDate:           "2024-05-01"
  )
  {
    IsSuccess
    Message
    Status
    Result {
      colorlegend
      tif
      png
      json
    }
  }
}