The agricultural sector is going to face enormous challenges in order to feed the 9.6 billion people that the FAO predicts are going to inhabit the planet by 2050: food production must increase by 70% by 2050, and this has to be achieved in spite of the limited availability of arable lands, the increasing need for fresh water (agriculture consumes 70 percent of the world’s fresh water supply) and other less predictable factors, such as the impact of climate change which, according a recent report by the UN could lead, among other things, to changes to seasonal events in the life cycle of plant and animals.
One way to address these issues and increase the quality and quantity of agricultural production is using sensing, acting and AI learning technology to make farms more “intelligent” and more connected throughout the so-called “precision agriculture” also known as ‘smart Agriculture or Smart Farming’.
Blockchain in Agriculture
Community Agriculture (Smart Contracts)
Community supported agriculture is an alternative economic model for the production and distribution of locally grown food in which a community of shareholders funds the operation of a local farm at the beginning of the growing season in exchange for weekly deliveries of fresh food products (such as eggs, dairy, meats, etc). Although Farmshare has made a start with using Blockchain technology to create CSAs, there’s still huge potential for farming communities around the world to adopt this model of finance and distribution.
Tracing Origin of Products (Transparent Transactions)
By establishing a Blockchain-driven ecosystem for the registration, payment, and transport of crops or other agricultural products, buyers can also verify that the product they are receiving is exactly what they paid for. With every step of the transaction process recorded on the Blockchain, if a supplier claims that its coffee beans are ethically sourced from Colombia, for example, this can easily be confirmed by tracing the journey from farmer to coffee shop.
IoT and Agriculture
Global demand for agricultural products is on the rise. By applying advanced technologies in agricultural production, farmers are able to measure and manage the variability of crops in the fields and animals within the herds. Connected equipment, sensors and controllers are being deployed across farms worldwide to increase yield in order to meet the growing demand for food driven by population growth and urbanisation.
The agricultural sector is significantly underpenetrated by IoT technologies but set to experience a wave of technology adoption.
Most farms are still family-run businesses and often employ an informal style of management. The adoption of precision farming solutions and software is demanding growers to learn new farming practices and become more organised. In addition, the increasingly complex technological environment that farmers operate in demands dealerships to offer a greater extent of services to integrate and support the range of technologies that are utilised in precision farming. This is increasingly addressed by established precision technology companies. They are actively investing in their channel partners to offer enhanced support for their precision farming portfolios. Increased professionalisation of the industry is likely to continue and result in stronger focus on yield maximisation and cost efficiency, which are proven advantages with using precision technologies.
Deep learning and Agriculture
“The industry will be transformed by data science and artificial intelligence. Farmers will have the tools to get the most from every acre.”
AI versus hungry bugs
Pests have always plagued farmers. But AI gives growers a weapon against cereal-hungry bugs.
A farmer in Texas checked the direction of the wind and reckoned a swarm of grasshoppers was likely to descend on the southwest corner of his farm. But before he could check his crops, the farmer got an alert on his smartphone from the AI and data company he hires to help monitor his farm. Checking new satellite images against pictures of the same parcel over a five-year period, an AI algorithm detected that the insects had landed in another corner of the farmer’s field. The farmer inspected the section, confirmed the warning was accurate and removed the costly pests from his field of nearly ripened corn.
Today city farming is gaining its popularity among the urban population. Dubbed vertical farming is one of the leading directions of city farming since it makes it possible to grow fruits and vegetables right indoors in the downtown of a big city. In other words, such harvest can be grown on walls and roofs of buildings. This tendency seems to be a solution to the problem of lack of nutrition. In addition, such technique allows growing crops 20% faster and use 90% less water. Thus, this method can be applied as well even in a very dry region like Eastern Africa.
Smart sensors come to rescue
They can monitor all plants’ vitals, send all necessary data in a real-time mode. Here is where machine learning comes in. Algorithms check all information, explore it to predict what pests can attack it. PVC pipes equipped with sensors, lights, cameras etc. are located around the perimeter of the field or greenhouse. All information is sent to the server where machine learning algorithm processes this information and analyses the whole process.