This project is increasing the productivity and yield of Australian horticulture farming operations by improving the quality and accuracy of weather forecasting information, considering the grower’s unique terrain and operational requirements.

This project will develop and trial an on-farm machine learning and artificial intelligence (AI) data-driven hyper-localised weather forecasting platform for Australian horticultural growers and provide tangible results that will directly inform the commercialisation of the Jane’s Weather platform, enhancing the industry’s weather forecasting capabilities.

Growers constantly grapple with complex decisions that impact their resources and day-to-day operations in the field. Unfortunately, these decisions are often based on unreliable, inaccurate, or limited-scope weather information. The existing weather data is not localised enough, and conventional forecasts lack the necessary level of detail for effective planning.

Addressing these challenges requires a paradigm shift. Integrating AI and machine learning into hyper-localised weather forecasting empowers farmers with more accurate, contextually tailored insights, enabling them to make informed decisions about frost protection, spraying practices, irrigation, growth management, and pest control.

This project will develop a transformative approach that will bring greater precision to farming operations by bridging the gap between data scarcity and the intricacies of agricultural demands.