HEMAV

The likelihood of a farmer’s yield being profitable is as good as flipping a coin and wishing for heads. The agricultural sector is primarily exposed to unpredictable weather, uncertain yields, and continuous fluctuations in input prices, preventing it from ensuring quality products every time. Against this backdrop, the agricultural industry is in increasing need of forecasting models to obtain high-precision actionable data and sustainably manage current and future yields.

HEMAV brings much-needed certainty to agricultural productivity with its groundbreaking digital SaaS platform, LAYERS. The cutting-edge predictive analytics platform provides farmers with actionable data through its predictive models to help them gain control over their yields.

"We improve field productivity by integrating comprehensive predictive capabilities into a customized model that processes agronomic information to deliver accurate, actionable data that ensures superior yields every time," says Xavier Silva, CEO and co-founder of HEMAV.

LAYERS by HEMAV is a consolidated precision agriculture tool that creates customized AI forecasting models to remotely monitor land for the delivery of predictive KPIs about land efficiency, which enables better decision-making for farming. The integration of LAYERS into clients' existing farming tools provides weekly alerts to the growers. Through smart sampling, monitoring, and forecasting, LAYERS offers unique, customized, and statistical alerts with deeper insights about field conditions, along with soil, crop, and plant health analysis to increase the land’s productivity. All agronomic information is presented on a single platform with 97 percent accuracy, predicting production quality and quantity to secure yield ahead of time.

The LAYERS’ Analytical Dashboard and Map Viewer generate predictive capabilities by storing agronomic data of every field through an anomaly analytics approach. The temporal comparison between maps enables easy decisions on appropriate field selection, harvest timing, and fertilizer treatment.

HEMAV’s team takes up to two to six weeks to implement LAYERS into the client's existing farming system. During integration, Hemav assigns customer project management specialists for farmers to provide support in monitoring the models weekly to verify if they are providing relevant data on soil, water, and fertilizer requirements with the utmost precision.

A case study further substantiates HEMAV’s state-of-the-art solutions. The Gloria Group, a large sugarcane conglomerate with 70,000 hectares of sugarcane in America, Peru, and Ecuador, has been using the LAYERS operating system for three different purposes over the past four years.

They use SAT-TECH, a remote sensing tool embedded in LAYERS, to get up-to-date information on the development of their crops and soil nutrient recommendations to plan weekly field treatments. In parallel, the Gloria Group uses LAYERS PREDICTIVE-TECH, an AI-powered custom model, to predict global forecasts of annual crop production and quality estimates, which has dramatically improved their crop logistical planning system that ranks crops by sugar content.

The Gloria Group also uses HEMAV's DRONE-TECH, an aerial monitoring tool that helps farmers gain insights into gaps and weed growth in the field. Based on the data provided by DRONE-TECH, they could easily detect and remove weed roots from sugarcane plants 40 days after sowing to control weed growth and maintain soil nutrient balance. This feature has empowered Gloria Group to ramp up their field production by three to ten percent.

HEMAV organizes logistics and agronomic data for sugarcane, cotton, corn, soy, and sugar beet production, which helps in increasing productivity while promoting agricultural transparency and sustainable precision farming. HEMAV has published a research paper, which validates the efficacy of predictive analytics in eliminating the uncertainty of quality yields in the agriculture sector. Operating in 20 countries, it aims to help more and more farmers grow their businesses by improving their yield production to achieve maximum profitability and sustainability.