March 26

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Digital innovations will reinvent agriculture


Today’s agriculture uses robots, temperature and humidity sensors, aerial images and GPS technology, among other things, which has already made farming more profitable, efficient, safe and environmentally friendly by focusing on :

 

  • Repetitive tasks
  • Data collection

·         Sensors are used to collect data on the soil, water management, plant growth, weather conditions, etc.

  • Foresight

·         For a variety of situations, agricultural forecasting tools are already very effective.

 

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In the future, farmers will massively use mobile devices, cloud computing, IoT platforms, artificial intelligence, robots, drones, intelligent sensors, 3D printing, augmented reality, wearable computing, localisation technologies, metadata analysis, highly complex algorithms, blockchain, energy harvesting, authentication and fraud detection, the relationship with the end customer, and interfaces for human-machine interaction. The aim is to optimise crop yields and reduce waste.

 

Precision agriculture

The most important innovation will be precision farming. This involves monitoring crops using remote command and control systems, autonomous machines, etc. to obtain precise data to improve and increase the quality and quantity of production at the lowest possible cost. The aim is to produce more, of better quality, at lower cost and, above all, with fewer resources.

For example, remote sensors placed in fields allow farmers to obtain detailed information such as soil acidity, temperature and humidity, which can be used to predict weather conditions for the days and weeks ahead.

 

Precision farming will make it possible to make the best decisions, which logically means the best agricultural production, in addition to collecting and analysing data. Everything will be anticipated, controlled, measured, and constantly interacted with by all the players in a marketplace that brings together all the players involved in the agricultural product production, distribution, and marketing chain.

 

Vines are an excellent example of precision agriculture. Thanks to the precision of sprayers, the quantities of products dispersed have been divided by 4. Soil life is gradually returning. This requires significant investment and additional labour costs, which robots and artificial intelligence will eliminate progressively.

 

Connectivity

Data and connectivity in agriculture could add $500 billion in additional value to global gross domestic product by 2030.

Smart farming and precision agriculture rely heavily on IoT devices to support communication between field data and smart devices used for farm management.

Farmers can implement various tandem-working technologies, such as mobile applications, automated equipment, drones, sensors, and data transmission.

Faster two-way communication and more accurate field operations by sharing data faster and more efficiently.

The ability to make better decisions faster while improving machine performance for greater productivity, efficiency and yields.

 

Drones

The use of drones will make it possible to create electronic maps of fields in real-time, rapidly monitor crop conditions, track field performance, forecast crop yields, and carry out environmental monitoring of land.

Land use maps will be used to assess the state of the environment and monitor land use. The aim is to increase yields, improve the agrochemical properties of the soil and reduce financial costs through the optimal use of seeds, fertilisers, plant protection products and fuel.

 

For example, drones will make it possible to:

  • Identify plants that are deficient in nitrogen, which will grow more slowly and, therefore, need more of this nutrient.
  • Identify plants suffering from disease or in need of irrigation.
  • Detect herbicide-resistant weeds in the field.

 

Robots

Agricultural robots aim to boost production such as harvesting and scouting. For example:

  • Nursery planting
  • Crop sowing
  • Crop monitoring and analysis
  • Fertilisation and irrigation
  • Weeding and spraying crops
  • Thinning and pruning
  • Autonomous tractors
  • Milking
  • etc.

 

We will see an increasing number of miniature robots that can work in soil conditions that prevent the use of heavy machinery and work permanently in “swarms” in a coordinated way, day and night.

 

Robotics in agriculture is a global market worth more than $5 billion, and is set to double over the next five years.

 

IoT and sensors

By 2050, a typical farm is expected to produce 4.1 million data points every day. The explosion of IoT devices in other industries could pale in comparison to the opportunities in agriculture, already a $11.4 billion market.

These devices can consist of several sensors that examine, for example, several parameters influencing plant development in parallel (soil moisture, temperature, or electrical conductivity of the soil). The data can be used to monitor plant conditions in real time and predict when irrigation, fertilisation, or pesticide use will be required in certain areas.

 

Artificial intelligence

Artificial intelligence will be used extensively in agriculture to automate operations, reduce risks and make farming easier and more efficient. For example, AI will be increasingly used to :

 

  • Identify seeds that respond to climatic conditions.
  • Provide varied advice and recommendations.
  • predict weather conditions and other agricultural circumstances, such as land quality, groundwater, crop cycle, and pest attacks.
  • “train’ robots by ‘scanning’ numerous crops in different circumstances or analysing tens of thousands of images representing targets, e.g. crops and weeds. The information is then automatically analysed by neural networks and used to program robots to treat each plant and each patch of soil using deep learning.

Neural networks will also be increasingly used in agriculture to recognise relationships between images, thanks to a process that mimics the way the human brain works.

 

Last words

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