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Labor Use Cases

Assigning Thinning Labor

• 5-20% improvement in labor efficiency

• Previous customers saved $27,000 just on labor for a single 20 acre block

• Higher yields & improved crop vigor due to educated thinning 

During blossom and fruitlet stages, utilizing the Green Atlas Cartographer will inform growers where to thin and how much to thin based on cluster & fruit counts. Scanning an orchard block will allow the grower to visualize the highest degree of cluster & fruit counts throughout the orchard allowing them to navigate their employees to area’s that will inevitably take more time and energy.

Raw Data

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Cluster Size enumerated by row

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Increased fruit quality & consistency. Uniformity achieved by quantifying canopy size & light exposure distribution.

Pruning and thinning directly effect crop size and fruit quality. Ex: too much fruit left on the tree causes less nutrients to properly disperse and the fruit ends up being too small and vise versa.

Quantify number of clusters distributed on trees, the size of fruit, density of the canopy and leaf-area to inform growers where labor should be focused.

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Accurate & early predictions of yield, Increased fruit quality & control of what’s picked/shipped. Growers can allocate labor for picking.

Yield estimations are crucial not only for the grower but also packing facilities, warehouses, and sales desk benefit from them in order to calculate how much is being sold, how much storage they need, and potential profits.

Based on total fruit, size, and color- Growers know where and when to focus labor and how much fruit is expected for harvest.


Bin Placement & Estimates

Grower, packing warehouse, sales and marketing teams are on the same page of how much fruit production is expected while saving on labor. ~ 2% error

During harvest, the amount of bins and bin placement needed for a block inevitably controls the size of labor force used for picking and how much fruit is processed in storage. 

Based on fruit size and quantity we are able to predict how many bins are needed for a given block and where to place them based on fruit density.

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Yield Use Cases

Irrigation Planning

Decreased tree stress by installing double-drip, emitters, & mulch based on soil texture distribution. Confidence in soil moisture sensor placement for optimal irrigation scheduling.

Irrigation planning is crucial to tree and fruit heath/quality. Irrigation is dependent upon the tree’s needs and not every tree in a single block needs to be watered the same.

Through the use of Soil Optix we are able to determine soil types and where certain areas need to be watered more or less

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Nutrient Optimization 

Precision Ag approach to optimize nutrient application, identify where there are deficiencies, all with the aim to improve uniformity in crop yields & vigor.

Being able to quantify yield outcomes based on nutrient availability. 

Establish relationship between soil variables including texture, pH, micro/macro nutrients vs. yield: size & count.

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Shade Cloth Distribution

Less damage caused to fruit from sunburn, resulting in higher yields. Overall less shade cloth used resulting in money saved on resources and labor.

Due to sunburn and obtaining optimal fruit color shade cloth can alter the orchard climate and reduce stressful conditions caused by excessive light and heat. 

Based on tree canopy density and height we are able to identify potential problem areas that are susceptible to sun burn and colorization flaws.

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Missing Pollinators & Distribution

Increased yields & improved fruit quality by properly placing trees. Saved time and money on labor to identify missing tree locations.

Pollinator distribution and location affect crop yields and fruit quality. This results in a yearly task where employees check each row to identify where there’s missing or a surplus of pollinators. Often times this task isn't fully completed resulting in missing pollinators.

Preferably done in the off season we use Green Atlas to identify pollinator distribution at a customizable range that meets the growers preference.

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