Cultivation planning, monitoring and yield forecasting for agricultural enterprises

This R&D has the potential to help famers save money by purchasing agricultural commodities at the right time, in the right place: Photo credit: Gregory Hayes

Agrovisio and Weather Logistics provide complementary solutions for agricultural enterprises that can be used as a tool for cultivation planning, monitoring and yield forecasting to enable more productive, profitable and sustainable agriculture. Agrovisio uses in-house soil maps, in-house growth models learned for various crop types and satellite imagery to detect crops, monitor their status and classify them in parcel detail to disclose crop based cultivation maps, estimate yield and suggest field selection for the upcoming season. Founded in 2017, Agrovisio has an interdisciplinary team having more than 23 years of academic background highly skilled in soil science, remote sensing, GIS, computer vision, machine learning and deep learning. Improved long-term weather predictions and field-level climate prediction is strongly aligned with Agrovisio’s vision to build on its yield forecasting service in Turkey. Weather Logistics offers expertise in analysing complex environmental data, weather and climate prediction and model verification / validation. Its unique seasonal forecast system, developed through 5 years of intensive R&D delivers daily weather data and confidence intervals at the field-level. Tailored for large-scale grower groups and producers operating in the United Kingdom, the vision is to scale this technology capability to solve challenges in Turkey and elsewhere. Forecasts are designed for cost-benefit based decision making; either to incorporate into crop development models, for fresh produce supply-demand management, to apply early frost protection, advising on preventative spraying against disease or to refine variable-rate fertiliser applications. Its proven accuracy is achieved by combining multiple weather and climate data, weather prediction models, and its own algorithms to deliver all-year-round predictive skill. 


A collaboration between these parties covering soil, weather and crop monitoring would greatly help manage the value chain, take measures where necessary, and forecast production and financials early on. These analyses can guide agricultural organizations with quite more accurate data in (1) adaptive selection of fields, (2) risk and productivity mapping, (3) growth tracking, (4) yield forecasting and harvest management. Even more, covering soil, weather and crop monitoring at once will enhance current solution with scalability to adapt in different climate zones; and brings potential of global expansion.


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