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Palm Oil And Machine Learning

Palm Oil and Machine Learning

When we look up at the sky today to see the stars and the moon, there are around 4987 orbital satellites looking right back down at us, and capturing images of vast tracts of the earth. These eyes in the sky provide image data that can tell us a number of things about life on earth, including how fast our building developments are progressing, how weather conditions are likely to affect our daily lives, and how well our crops our growing.

(Image: Craig/Public domain)

One crop’s spread in particular is being watched closely, and that is the oil palm tree (Elaeis guineensis), whose oil was first used 5000 years ago in Egypt. Today, it joins us at the breakfast table in the form of cereal and bread, and stays with us until we (hopefully) brush our teeth and call it a night. We each consume over 15 pounds of palm oil annually. This oil has seen a meteoric rise as a versatile resource. It can handle heat and changes of form. It is low cost, and is the highest-yielding vegetable oil crop. Processed versions of this oil go into everything from candy and ice cream to biodiesel for cars. Today, 85% of the world’s supply comes from Malaysia and Indonesia, who have grown their economies on the oil palm’s popularity.

So what’s the problem?

The glisten wears off when we become aware of the steep cost of meeting global demand for this commodity. Hundreds of thousands of acres of biodiverse forests in regions like Borneo were destroyed to widen the land available for palm cultivation. This rampant deforestation has pushed species like the Sumatran tigers and orangutans towards extinction.

(Image: Flickr/glennhurowitz)

Local populations suffered as the method of clearing land by starting huge fires set off a health crisis in Indonesia. The process released unprecedented levels of carbon into the atmosphere. NASA research cited the destruction in Borneo as a contributor to the largest single-year global increase in carbon emissions in two millennia.

Awareness about these hazards has now spread. The Roundtable for Sustainable Palm Oil (RSPO) was set up in 2004 to promote the adoption of more responsible cultivation practices. In 2010, around 400 companies also signed a Consumer Goods Forum pledge to achieve zero net deforestation in their supply chains by 2020. The pledge covers other commodities like soy, beef, and timber, but the palm oil industry accounts for about 59% of the commitments.

Enter Machine Learning and Computer Vision

With this deadline coming up, companies like Unilever and Nestle are using satellite images to study deforestation and keep a closer check on their palm oil supply chain. Satellites and drones can capture the oil palm’s whereabouts and rate of proliferation as well as the areas where it is replacing natural forests.

(Image: NASA)

Geospatial datasets can be expensive and companies need to extract the most actionable insights possible. Highly annotated datasets are used to train deep learning algorithms and unlock these insights. Companies must make strategic decisions about what they want from the data they are working with. They must select from smaller quantities of accurate, precise and specific data (like individual trees), or more plentiful but less precise broad datasets. They must also design for edge cases, whether from a corrupt image or a unique looking plantation or trees.

Once the scope of the project has been defined and the data assembled, a professional image annotation team trained in pattern recognition is required to make sense of the hundreds of satellite images. The most basic requirement is discerning a palm tree from other trees, and distinguishing a plantation from other vegetation, particularly natural forests. To be successful at this seemingly-simple task, a data labeler needs be trained with and exposed to various oil palm plantations using different satellite imagery resolutions, lighting conditions and other variables. Marking a tree is easier to achieve with high resolution imagery. It becomes more challenging at medium resolution where one pixel is between 3 and 6 square meters, and it is in these cases that a labeler has to draw upon the specialised training and experience.

It takes a practiced eye to spot a palm tree when canopies overlap in a crowded plantation, and to mark each tree’s crown limits accurately. A labeler can also use polygon marking to indicate the entire plantation’s limits. Another common method is point annotation on each tree center. Marking patterns like color, shape, and size helps in understanding the health of the plantation. Malaysian plantations, for example, show a great variety in plant density and spatial arrangement. A data labeler studying the datasets closely over time can also observe the areas where growth takes place, adding even more useful insights.

Tracking palm plantations to prevent rampant deforestation is not the only use case use for this technology. Imaging technology is increasingly being leveraged to tackle global crises like climate change. A satellite is being developed that can map the emission of methane anywhere in the globe with precision. The State of California announced that it is building a satellite to locate and regulate sources of pollution. Initiatives like Microsoft’s AI for Earth equip those fighting the good fight with the best in cutting-edge tools. Companies like Planet scan the surface of the earth at regular intervals and make datasets and tools available for analysis. Specialists like Orbital Insight work with carefully labeled data to deliver the insights at scale.

So look out of the window at the sky when you brush your teeth tonight and think of palm oil!

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