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  • Writer's pictureAggie the Intern

Enhancing Sugarcane Farming with Drone Surveys: A Case Study

If you're a farmer, or a thinking of becoming a farmer, then optimizing crop production is of high importance to you. Optimizing crop production means ensuring that each acre of your farm is producing the maximum amount of food it can produce without wearing out the land. This is important from a business perspective because maximizing crop production means you are maximizing revenue per acre. It is also important from a cost perspective, because it means you need less land to produce the quantity of food you desire.

Did you know, 50% of the crops you could have harvested won't make it out of the farm? This is called pr-harvest loss. Your crops will be lost to pest, diseases, theft on the farm, and poor handling of crops during harvest. Pest and disease are the biggest drivers of pre-harvest loss.

This article explains our interesting approach to surveying the farm so you can reduce your losses significantly.


Did you know, 50% of the crops you could have harvested won't make it out of the farm?

We recently tackled a project using drone technology to boost sugarcane farming efficiency on a 20-hectare sugarcane field. Our mission? To pinpoint areas of the farm showing signs of stress (ie, disease or pest infestation) using nothing but a trusty DJI Phantom 3 drone armed with RGB imaging capabilities.




Now, before we dive in, let's talk data. In agriculture, knowing the kind of data you're collecting is key. There's RGB - like the pictures your smartphone takes, and then there's multispectral, which goes beyond - capturing near-infrared for a deeper insight into crop health. With only RGB imagery at our disposal, capturing the near-infrared wasn't in the cards.


To process the images collected by the drone, we used PIX4D Fields, a nifty software to stitch together our drone images into a flat image of the farm, aka an orthomosaic. And here's where the magic happened, literally! PIX4D Fields boasts a slick 'Magic tool' feature powered by computer vision and machine learning. It's like having a digital sleuth👀 for spotting sick crops in a haystack.


We tagged sections of the farm showing stressed sugarcane, and voila! The Magic tool did its thing, highlighting even more trouble spots across the field.


Image above is the final map of the farm, where red highlights sections of the farm that showed observable signs of disease.


Where do we go from here?

We could use this new map to create a spraying map for the farmer. A spray map allows us to define different amounts of pesticide to apply to different sections of the farm depending on the stress levels of the crop. We call this a Variable Rate Application map.

We can load this map onto our pesticide-spraying drone and it will automatically fly over the farm applying different quantities of pesticide to the farm depending on the spray map given to it. This saves the farmer costs on pesticide by only using pesticide where it’s needed.


This project was a validation of drone tech's potential to revolutionize farming as we know it. From pinpointing trouble spots to fine-tuning crop spraying, drones are proving to be our farming superheroes.


Until next time, happy farming, y'all!


Aggie the Intern

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