Change in the agricultural sector becomes necessary as society advances. The context of the agricultural industry at the beginning of the 20th century has nothing to do with today, where the main actors have a new companion in technology.
Technology has significantly impacted the agricultural sector in recent years, giving farmers access to information and tools that enable them to improve efficiency, reduce costs and increase production.
Some of the most widely used technologies in the agricultural sector include:
- Sensors and drones: Sensors and drones collect information on weather, soil and crop health. This allows farmers to decide when and how much water, fertiliser and pesticides to apply.
- Smart irrigation systems: Smart irrigation systems use sensors to measure soil moisture and adjust irrigation accordingly. This helps to conserve water and reduce production costs.
- Precision agriculture: Precision agriculture involves using technologies such as remote sensing, mapping and modelling to help farmers make informed decisions about managing their land and crops.
- Agricultural robots: Agricultural robots are used for planting, cultivating and harvesting. This reduces reliance on labour and increases the production efficiency.
- Farm management platforms: Farm management platforms allow farmers to collect and analyse data from different sources to make informed decisions about managing their land and crops.
In short, technology has significantly impacted the agricultural sector and enabled farmers to improve efficiency, reduce costs and increase production. As technology continues to advance, we are likely to see even more innovations in the agricultural sector in the future.
Within this new paradigm, at the end of 2020, together with other colleagues, I was concerned about adapting agriculture to the current market through efficient use of resources while respecting the environment. In a research paper, we worked on the most widely used approaches to analyse the efficiency and sustainability of farms by analysing data based on production efficiency with techniques such as Data Envelopment Analysis and Stochastic Frontier Analysis, as they allowed us to see how efficiently outputs are generated independently of the units of measurement of the inputs. The paper presents a real scenario to make farms more profitable and sustainable by applying the Internet of Things and Edge Computing. What already made our model enjoyable is that it allowed us to monitor environmental conditions with real-time data from the different sensors that have been installed on a given farm, minimising costs and gaining robustness in the transmission of data to the cloud with Edge Computing, and thus having a complete picture in terms of monthly resource efficiency.