El IoT and new technologies in general have changed the way farming is done.. In fact, little by little a multitude of technologies are being implemented in the field to improve production, or to achieve better yields, comfort for farmers, etc. Therefore, in this article we will show you The potential of Agriculture 2.0 with some examples.
In addition, all those who want to be part of this new transition and modernize their agricultural production, you will be able to get good information and ideas to get started.
What is Agriculture 2.0?
La Agriculture 2.0, also known as precision agriculture or smart agriculture, represents a radical transformation in traditional agricultural practices. It involves the application of information and communication technologies (ICT) to the agricultural sector, with the aim of optimizing production, reducing costs and minimizing environmental impact.
It is true that agriculture is no longer what it used to be, even before the arrival of the so-called 2.0, since the genetics of transgenic seeds, chemical phytosanitary products, artificial fertilizers, etc., have already completely destroyed the sector. More is produced, yes. But it is also true that what is produced is less healthy. This, together with the low prices paid in the fields, has put the sector in check, leaving it on the edge of an abyss, since many lands are not profitable and farmers are increasingly making less profits, or even making losses, while politicians look the other way, encouraging the purchase of products from other countries and strangling national ones.
The new era of Agriculture 2.0 has therefore arrived at a time of uncertainty, providing solutions that are not the basic ones for the sector to return to what it was, and which provide more benefit to the corporations that sell the technology than to the farmer in general, even more so taking into account that many are older, are not digital natives, and adapting to it is a great challenge for them, a learning curve that often does not compensate. However, For new and prospective farmers, there might be some key points interesting:
- Data: possibility of collecting and analyzing large amounts of data from sensors, satellite images and other devices, either locally or through Big Data.
- Automation: use of machinery and autonomous systems to perform agricultural tasks efficiently and accurately.
- Connectivity: interconnection of devices and systems to facilitate communication and information exchange, with the help of the new paradigms of cloud, fog and edge computing, and IoT devices.
- Artificial intelligence: application of machine learning algorithms to make data-driven decisions, or analyze the situation of crops, diagnose potential problems, etc.
Between the advantages We have contributed:
- Greater efficiency: optimization of the use of resources such as water, fertilizers and pesticides, which reduces costs and increases productivity.
- Lower environmental impact: reducing pollution and preserving natural resources.
- Higher quality of products: production of safer and more nutritious foods.
- More informed decision making: Farmers can make decisions based on real, real-time data.
- Adaptation to climate change: development of more resilient and sustainable agricultural practices.
How can open source and hardware libre to Agriculture 2.0?
El open source software and the hardware libre They play a fundamental role in the democratisation of Agriculture 2.0, by offering a series of advantages over proprietary programmes, providing farmers who implement an Agriculture 2.0 development plan with greater accessibility, without the need to pay licences, with the possibility of being adapted or modified according to the needs of each one, with total transparency to improve reliability, security and confidence, as well as avoiding dependence on large corporations.
Technology use cases in the agriculture sector
Agriculture 2.0 has experienced exponential growth thanks to the integration of various technologies that allow for the optimization of agricultural production and the minimization of environmental impact. Below, we will explore some of the most relevant technologies and their applications:
Machinery
La machining automation reduce the work involved in sowing, fertilization, harvesting or final product processing processes, with greater productivity, precision and efficiency, as well as lower costs, using for example robots, artificial vision systems, etc.
Some Agricultural vehicles are also becoming autonomous, without the need for a driver, which can facilitate and improve farming or harvesting tasks, improving the routes or layouts made using LiDAR and AI systems, etc., also reducing the amount of fuel or energy needed.
On the other hand, you can also monitor and perform centralized traffic control in production lands, avoiding congestion in some areas, optimizing the workflow so that everything arrives at the optimal time, improving safety, and reducing damage to crops caused by the passage of heavy machinery.
Irrigation
La Water scarcity is a global challenge which significantly affects the agricultural sector. To address this problem, various innovative technologies have been developed that allow irrigation to be optimized, reducing water consumption and improving crop efficiency.
For example, you can have several sensors that send the data they collect wirelessly and are placed in different areas of the field to measure soil moisture in real time, and thus activate irrigation by sector just when it is needed. Weather stations can also be used to obtain detailed information on weather conditions, such as rainfall, temperature, relative humidity, etc.
In addition to this, there are advanced drip or micro-sprinkler irrigation systems that are much more efficient and can be activated by timers or controlled selectively by software, to water only where needed.
Pre-harvested, harvested and post-harvested
The Drones have provided innovative and efficient solutions for various tasks, from field treatment to harvesting and post-harvest. Their versatility and precision make them indispensable tools for optimising agricultural processes and improving productivity. For example, they can be used to apply phytosanitary products more efficiently, spraying those areas where they are needed, and even equipped with vision systems so that they can detect pests or diseases that need to be treated very early, before they affect the entire crop.
After harvest, technology can also help determine the capacity of warehouses and silos, recognize product status, select samples for quality control, keep records, etc.
Computational models
The computational models By offering sophisticated tools for analyzing vast amounts of data from a variety of sources, these models enable more informed, evidence-based decisions. For example, they can more accurately predict future crop yields by analyzing historical data on climate, soil, and farming practices. This information is crucial for adjusting farming practices to maximize yields, or even predicting things before they happen, or helping to assess the impact of different practices before they are implemented, based on simulations.
Another important application of computational models is the crop rotation design. By analyzing soil characteristics, climate and previous crop rotation, models can select the most suitable crops for each plot, improving soil health and increasing crop diversity.