The concepts of precision agriculture (in which machines apply the exact amount of pesticides/nutrients, irrigation on demand etc.) and the use of digital technology in plants are experiencing a moment of great growth, with new technologies being developed and improved to increase productivity in the field. Despite the delay in the entry of digital technology in Agribusiness (in relation to other economic sectors), there is no going back and the trends show that agriculture and technology go hand in hand. Just to give an idea of the growth in the use of technology in agribusinesses, in 2019, Radar AgTech Brasil (a report and mapping of Brazilian Agribusiness startups) identified 1125 agriculture technology companies.
Given the importance of the agricultural sector in Brazil’s economy, Venturus is always analyzing technological trends in Agribusiness. The list below presents some of the trends that we believe should guide technological development in agriculture in 2020:
The Internet of Things (IoT) — connected objects that can perform tasks or collect data through sensors — is one of the major focuses of technological development in Agribusiness. However, we can already see a trend towards more specialization in the Internet of Things, going beyond sensors, event detection and sending data to a server. One of these specializations within IoT is Edge Analytics.
In a practical way, Edge Analystics analyses data collected from sensors or IoT equipment. Thus, IoT devices no longer simply monitor and send data to a central where processing is performed. Therefore, Edge Analytics involves the collection, analysis and processing of data on the sensor itself (based on IoT data, for example) and making decisions on the edge, that is, within the physical equipment that generated the data.
The advantages of using Edge Analytics are:
- Obtaining data (temperature, humidity, nutrient content of plants, among others) in real time using IoT devices for visualization and decision-making;
- Dramatically reducing the amount of data traveling across the network to a cloud. Thus, decisions are made on the edge, with faster action.
However, Edge Analytics is a relatively new technology. This means that not all hardware is capable of storing all of the data collect and performing complex processing at the same time. That means that Edge Analytics requires the use of more powerful hardware and equipment. In other words, time is gained in agility and immediate responses and actions of the equipment used, but, at the same time, more sophisticated equipment/hardware is required.
Edge Analytics could be used in Agribusiness in irrigation equipment. The equipment could receive IoT sensors with built-in software that would make the decision to irrigate the detected stretch or not locally, based on the information obtained from the equipment. The same type of mechanism could be considered for herbicides. The decision to spray it or not would be a result of the analysis performed in the devices.
Agriculture 4.0 is a term that comes much more from the manufacture industry than the agricultural sector. After three industrial revolutions (coal, electricity and electronics), we currently live in a fourth phase of technology. The fourth industrial revolution has a deep and exponential impact and is characterized by a set of technologies that allow the fusion of the physical, digital and biological world.
Industry 4.0 has, at its base, pillars of technological advancement, namely: Big Data and Analytics, robot automation, simulation, augmented reality, system integration, additive manufacturing, cyber security, cloud and industrial internet. Within these new parameters the concept of Agriculture 4.0, a set of integrated and connected digital technologies through software, systems and equipment, is introduced. In other words, it is almost a parallel of Industry 4.0, only applied to Agribusiness.
This means improved monitoring of production, from the planning stage to harvest and delivery. This is done through instant data collected and stored at each stage of production, better management (with the use of more detailed information from each phase obtained online, without risk of losing information when storing data in the cloud) and control of production (linking all information related to production). All of these improvements allow financial and logistical control of the whole agricultural production, very similar to an industrial process.
Agriculture 4.0 has a lot to gain from taking inspiration from many of the factors already established by Industry 4.0. Precision agriculture, data from the cloud, complex and fast analysis, among many other factors, are part of the range of possibilities in Agriculture 4.0.
Here are some examples of expected gains with Agriculture 4.0:
- Improved property management;
- Waste reduction;
- Agricultural production cost reduction;
- Remote farm control;
- Productivity increase;
- Business sustainability improvement.
Connection Technologies (LPWA)
Connectivity in farms still presents one of the major bottlenecks for faster development of technology in agriculture. While some more advanced and financially structured rural properties have stablished their own connection networks, the vast majority of Brazilian farmers still have little access to the connection on their farms.
The arrival of 5G networks still brings great expectations in terms of improvements to connection and data transmission speed. However, in addition to the problems inherent to this technology (such as the need for a greater number of transmission and installation cost), more recent news from government officials indicate that these networks may only start to be implemented in 2022, given the recent postponements of the auction of the spectrum of 5G networks in Brazil. With the current coronavirus crisis, these 5G network implementation dates may suffer even longer delays.
As a result, communication networks such as LPWA (Low Power Wide Area) are still a strong focus of the development of technology for agribusinesses. What are LPWA networks? They are networks designed specifically for IoT applications, that use a low volume of data traffic, but allow equipment to consume less battery.
LPWA are technologies that allow long communication ranges at low transmission rates, with low energy usage (they require smaller and less powerful batteries). Thus, these networks allow simpler equipment to be used at the edge of the applications, since the amount of information to be transmitted does not require large volumes of data.
Terms such as LORA, Sigfox, NB-IOT, LTE-M, among others, are examples of LPWA networks available on the market. Telecommunication network companies themselves are providing different types of LPWA networks while 5G networks are not stablished. In an earlier post, connectivity alternatives in the field were discussed further. See our post for more information.
Real-Time Data Analysis
Real-time data analysis is a technology used to obtain information online, so that the farmer has access to the data and can make quick decisions. They are solutions, for example, that extract data from machines, speed, location, machine status, amount of product (such as herbicides) sprayed and other types of information.
With real-time data analysis, information can even be processed on a server or in the cloud, but with quick responses, so that production management is as efficient as possible.
In order to obtain data in real time, several other technologies are required:
Sensors, drones and alarms (IoT)
These are the equipment and the devices that collect and identify the data to be analyzed later. For example, images from drones, temperature and humidity sensors, among others.
Big data (data cloud)
It is the data cloud where the information obtained by the IoT equipment is stored.
Information system for farmers to make decisions
It is the system that groups the data obtained and stored in the network and that processes logic or software that helps the farmer. For example, a system that takes crop moisture information from the farm, processes it and indicates the best way to irrigate a given area.
Image Collection and Anomaly Detection
Another focus for the development of precision agriculture is the use of crop images to detect diseases, pests, planting problems, the need for fertilization, the need for irrigation in crops, among several other factors that affect agribusiness.
Crop imaging had a strong correlation with the use of satellites in the past. However, with the emergence of drones and local mechanisms more adept at capturing images, the amount of data being obtained locally makes the use of images an even more important factor in the efficiency of agricultural techniques.
Applying technologies such as Artificial Intelligence (in which, for example, plant disease data can be pre-detected with analysis of past production information, along with crop history), Machine Learning (which can detect leaf damage patterns and plant diseases, for instance), Neural Networks, among other techniques, allow data of great value to be extracted in the evaluation of rural production.
Recently, Venturus participated in a pasture project in which animals are allocated to pens by applying machine learning to the images of the pasture land. With machine learning based on the pasture state images, the system was able to detect the moment when the pasture was viable to receive the animals. This saves money (less use of people to go check the field), time (speed of information, only by photos) and generates quality of information for the producer to know the time to put the animals in the checked glebe.
Precision agriculture allows a more rational implementation of production processes, promoting the optimization of the use of inputs, increased profitability and minimization of environmental impacts. To achieve results that add value to the rural producer and the entire agribusiness chain, sensors, equipment, machinery and other equipment exchange millions of pieces of data every instant. This creates a need to protect the information that is circulating.
Digital criminals will be focused on information assets or network vulnerabilities. Intellectual property data, inside information, or even physical assets of agribusinesses may be affected by data manipulation and theft.
Among the main information assets in Agribusiness, we highlight enterprise management data, IoT sensor data and cloud data, among types of information that allow data to be either used by itself or data that allows processing on platforms of AI (Artificial Intelligence), analytics, cloud, among others.
All of this information in circulation, combined with the good moment of Brazilian Agribusiness, made having a data security treatment a necessity. This security of data can be performed in several ways, such as:
- Encryption of the data to be transmitted from the equipment to the cloud (at the code level);
- Use of tools to monitor the vulnerability of networks (monitoring accesses and possible intrusions);
- Backup guarantee (in case of data loss), ensuring strict control of access to data systems (so that only authorized people may enter the system).
The use of technology in Agribusiness is an irreversible trend. Each year, new uses of technology are inserted in solutions for agribusinesses and, thus, the challenges are also renewed. The implementation of technology in Brazilian agriculture has never been faster than it is currently. Certainly, the greatest advances in technology are being leveraged by the most advanced and qualified companies and producers. The challenge is to be able to allow increasing use of technology among medium and small producers, leading to more and more connected properties. Thus, the market for the use of new technologies in agriculture would have an exponential increase, besides helping in increasing quality and productivity in the field.
Venturus recognizes the importance of agriculture not only in national terms, but also sees the importance of Brazilian agriculture in global terms. Monitoring trends and possibilities to apply innovation in agriculture is part of the company’s DNA and, therefore, we are tuned in and attentive to agribusiness innovations.