Addressing sugarcane planting failures
Within the last few years, sugarcane has become very important in Brazil’s economy and in the world scenario. Brazil is the largest producer of sugarcane in the world. Sugarcane occupies 9.8 mio hectares, that represent about 12% of all cropland in Brazil. Brazil’s sugarcane sugar yields have grown at 3% CAGR since late 1970s, that prove numerous technological advancements in sugarcane cultivation, that happened over the last few decades. Yet, we believe that there is still an enormous potential to improve efficiency of the sugarcane production. Plating failures represent one of the main bottlenecks to increase a sugarcane productivity in Brazil.
Planting of sugarcane and failure rate
Sugarcane is grown by replanting part of a mature cane stalk. Farmers cut some of the fully grown cane stalks into lengths of about 40 centimeters. Sugarcane stalks are planted by special agro machinery, which drop them into furrows, add fertiliser and cover them with soil. Poor establishment of a sugarcane crop produce mediocre yields and result in a downward spiral in productivity. Successful establishment of the sugarcane will boost the productivity and profitability of an entire crop cycle over at least 5 to 7 years, as sugarcane is a multi-year crop.
Mechanized sugarcane planting has an average failure rate of 20–25%. This statistics is also proved by our existing commercial operations in Brazil. Assuming that 50% of the sugarcane planting failures can be addressed by the grower through a secondary replanting, an increase of up to 10–12% of yield can be expected for all subsequent growing seasons.
CaneFit Solution
Gamaya CaneFit solution offers a unique and efficient way to accurately detect and map sugarcane planting failures across your farmland. The CaneFit solution helps industrial sugarcane growers to better predict and manage risks associated with planting failures, and implement a secondary replanting where it’s needed to substantially increase production efficiency. Gamaya uses a patented ultra-compact hyperspectral camera to measure and precisely quantify the quantity and total length of the planting failures. The uniqueness of the Gamaya’s solution comes from the deep integration of our camera with the analytical software to process the data. The integrated solution from Gamaya can be used to analyze planting failures on thousands of hectares of sugarcane farmland.
The resulting analytics maps, such as the planting failures map illustrated above, are provided with the detailed statistics and the breakdown of the severity of the planting failures based on the total length of the planting gaps. The analytics maps are sent to the sugarcane grower for viewing in either Gamaya’s web platform or the farmer’s existing farm management software. The farmer can then analyze the fields for planting failures and create prescription maps that he can send directly to his machinery to perform replanting.
Gamaya approach
Gamaya technological advance comes from our cutting edge analytics that harvest the power of the hyperspectral technology. Gamaya is pioneering the use of hyperspectral technology either through drone or satellite for agricultural applications. The data acquired from Gamaya is 10 times more information rich than any other monitoring solution currently on market. This information in combination with our state of the art analytics guarantee that our products are more informative and accurate to similar products in the market.
Summary of our key competitive advantages:
1. Most sensitive and information-rich source of data, that provides us early access to high-quality data to develop crop intelligence on a global scale. Our unique patented hyperspectral cameras capture 10 times more information than multispectral cameras.
2. Ability to address multiple stressors globally while also providing localised customisation.
3. Ability to use AI and machine learning, facilitated by the high informational content of Gamaya camera, to continually improve existing products and develop new products. Multispectral imaging technology lacks the information richness to enable continual development.
