Situated within a global hub of innovation in artificial intelligence (AI), Motorleaf is a Montreal-based agriculture technology company founded in 2016 by CEO Alastair Monk, CTO Ramen Dutta and Director of Artificial Intelligence, Scott Dagondon. Producing software tools for controlled-environment agriculture production, Motorleaf leads in the development of artificial-intelligence services that enable farmers to better monitor and control their day-to-day operations. Of greatest importance are our machine-learning algorithms that assess growing conditions and predict crop yields in hydroponic commercial greenhouses for vegetable cultivation.
By collecting big data on past and current growing conditions, we train machine-learning algorithms to predict farm outcomes. Our first service enables accurate estimates of future harvest yields one to two weeks in advance for the main greenhouse crops of tomatoes and peppers. Subsequent products will include predictive algorithms for additional greenhouse crops and the onset of the common tomato pests, clavibacter and whitefly.
In their most basic form, our algorithms enable growers to automate yield forecasting of their harvests; however, when trained with sufficient data, our algorithms reduce errors in predicting the quantity of future harvest by more than 50%, where many customers obtain over 70% error reductions from the start. This is a ‘game changer’ for the greenhouse industry because we provide this first-of-a-kind solution that far exceeds the capacities of current, manual methods for harvest yield forecasting.
Our AI technology uses the same methods for predicting crop pests. Environmental stressors, such as temperature fluctuations and nutrient deficiencies, make plants vulnerable to disease. Our algorithms identify greenhouse zones experiencing environmental stress and thus are at risk of common diseases such as clavibacter and whitefly for tomatoes. With this knowledge, growers can intervene quickly to stop the spread of disease using less pesticides and sacrificing fewer plants.
Growers have few means to visualize data of their growing conditions, which makes it difficult for them to optimize growth protocols and make business decisions on past farming practices.
The Grow Journal collects data of growing conditions and stores a historical record of grow protocols associated with farm outcomes, such as harvest yield and quality. Presented in simple graphs and streamlined formats, growers use the Grow Journal to see what is working, what needs to change, and what went wrong if their harvests deviate from expected measures of quality and quantity.
Products fit for our customers' needs
All artificial intelligence products (predictive algorithms) are for large greenhouse producers cultivating tomatoes and peppers in typically, but not eclusively, 10+ acre facilities. (Large operations produce more of the data required to develop these products.)
The Grow Journal is applicable to all indoor farming markets, from large-scale greenhouses, to vertical farms, to one-room growth chambers used in research and development of new crop varieties.
Would you like more information concerning our products? Please contact our Customer Excellence Manager, Jennifer DeBraga, today: