Top AGTech Trends

1. Precision Agriculture

Precision farming is the application of precision agriculture practices. Precision agriculture technology is at the heart of the concept of sustainable agriculture. Systems access real-time data of the soil, air, crop status, and other pertinent information such as hyper-local weather predictions, equipment availability, and labor prices and are examples of IT and software services used by precision agriculture technology. Furthermore, technologies such as predictive analytics software may utilize the data to advise farmers on appropriate planting timings, crop rotations, soil management, and harvesting times. Drones are also increasingly being used in precision agriculture as they help manage the fields from above and can be used to make logical deductions.

Precision farming technologies thus assist the farmer in avoiding resource loss and preventing surface run-off- which leads to eutrophication – to guarantee that the soil contains the appropriate quantity of additives for optimal health. This may also be utilized to cut operational expenses and regulate the environmental effect of agriculture. The agricultural control centers may merge sensor data and image information from other data sources to offer farmers the ability to recognize fields that need treatment and then help decide the appropriate quantity of water, herbicides, and fertilizers to apply.

Precision agriculture is implemented using a variety of precision agricultural technology. These solutions decrease overlap, assure proper input placement, and protect conservation structures.  The systems mentioned such as drones, satellite imaging, artificial intelligence in agriculture, and IoT technologies are mostly used and they operate together. Precision agriculture uses technology to improve sustainability by making better use of land, water, fuel, fertilizer, and pesticides. Farmers that employ precision agricultural technologies effectively use less to grow more, lowering both costs and environmental effects.

Utilizing Satellite imagery for precision agriculture

Another developing trend is the utilization of satellite images for agricultural activities. These graphics are used to help farmers make better decisions. Because farming fields are reliant on location, software such as GIS uses satellite photography and becomes more important for sustainable farming. Farmers can chart current and prospective changes in agricultural yields, plant health, precipitation, temperature, and a variety of other factors. This precision agricultural technology enables GPS-based apps and intelligent machines to optimize corporate operations. Farmers can deal with specific regions of their while saving effort, time, and money.

NDVI technology

 Using satellite data, farmers may quickly monitor agricultural yields, detect crop risks, and conduct real-time field monitoring. The satellite can provide pictures, allowing the program to extract relevant information. The NDVI (Normalized Difference Vegetation Index) may identify withered crops, plant health, and vegetation content.

CCCI technology

 The Canopy Chlorophyll Content Index can be utilized to aid with crop nutrition. The NDRE (Normalized Difference RedEdge) can then identify the nitrogen concentration of the crops, which is necessary for healthy development. The (MSAVI) Modified Soil-Adjusted Vegetation Index is then used to reduce the background influence of the soil on the early phases of plant growth.

Farmers profit from precision agriculture technologies. These benefits can minimize fertilizer, nutrient, and water usage, reduce negative environmental consequences, reduce eutrophication in rivers, improve efficiency, lower crop prices, and so much more. This makes farming more sustainable, intelligent, and cost-effective. Satellite imaging makes use of two technologies to help in precision farming.

2. Predictive analytics in AgriTech

Once these datasets are merged, the software has more data about weather conditions and crop yields which can provide farmers with the expected results. More data allows the farmers to be aware of all sorts of information such as soil conditions, and weather conditions, and allow them to draft a viable business plan.

Once these datasets are merged, the software has more data about weather conditions and crop yields which can provide farmers with the expected results. More data allows the farmers to be aware of all sorts of information such as soil conditions, and weather conditions, and allow them to draft a viable business plan.

 Predictive analytics software can be used for data integration and collection. The integration can make machinery integrated with satellite data to advance precision agriculture. The weather can also be forecasted using predictive analytics software as the weather conditions can be predicted for cropping, planting or harvesting.

Data analysis can assist the farmers with real-time crop health monitoring, predict future crop yields, and also help make resource management decisions based on established trends which can lower waste and increase revenues.

The Advantages of Predictive Analytics in Agriculture

• Predictive analytics may be applied throughout the agricultural cycle, from crop selection through harvesting. Predictive modeling and analytics can be used to:
• Choose the best crop for your field: Farmers may make the optimal crop selection for any given circumstance by using soil analysis data, past weather, and other characteristics.
• Optimize irrigation – Analytics may help forecast crop stress times as well as ideal irrigation levels based on crop growth phases.
• Optimize land preparation: To optimize field activities, GPS-enabled field management maps may be connected with productivity maps.
• Improve crop protection: Using indicators such as soil properties and current weather conditions, predictive analytics may assist anticipate insect and crop disease outbreaks.
• Increase productivity and yields: Predictive analytics may be used to create management zones, optimize crop development, track season progress, and take appropriate steps.
• Avoid lower ROI – Predictive analytics can identify fields and subfields whose ROI is consistently lower and recommend if these areas should be removed from production.
• Reduce supply chain uncertainty: Unpredictable weather, strong storms, drought, and altering insect behavior as a result of weather are all environmental issues that influence the agriculture supply chain. Data may help farmers prepare for these difficulties by allowing them to make data-driven decisions.
• Reduce negative environmental effects: Predictive analytics may assist in understanding scenarios where environmental pollution risks are high, relating activities to their environmental imprint, and assisting in avoiding them.

3. Internet of Things

The Internet of Things is a technology that enables several machines to successfully connect. IoT software for farmers is a current option for creating a system that can monitor fields using sensors such as humidity, temperature, and soil, as well as automate irrigation systems. IoT also enables farmers to monitor their fields from any location and on any platform.

The need for IoT

The exponential growth in the world population means that the world will need to produce up to 70% more food by 2050 according to the UN Food and Agriculture Organization. This means that there is a critical need to enhance farming methods to make them more productive and maximally effective. Another concern over farming is that the structure is shifting as agricultural labor is declining, and recent examples are the great resignation. This is why internet connectivity can provide solutions for modern farming operations.

The Importance of IoT

According to the UN Food and Agriculture Organization, due to the globe’s exponential population increase, the world would need to produce up to 70% more food by 2050. This means that there is an urgent need to improve farming processes to make them more productive and effective. Another issue with farming is that the structure is evolving as agricultural labor declines, as recent examples show. This is why internet connectivity may help modern farming operations.

IoT technology can assist farmers in closing the supply-demand gap. This may be accomplished through maintaining profitability, good yields, and environmental preservation. This IoT-based technique can assist assure the appropriate allocation of resources to boost agricultural yields while also lowering operational costs. This is a type of precision agriculture since it maximizes the value of a business. Wireless connections, IT services, farming software, and specialized equipment are examples of IoT technology.

How does using IoT technology help farmers?

When farmers adopt IoT technology, they may improve their operational productivity while also reducing waste in fertilizer amount, machinery fuel usage, energy expenses, and water prices. This IoT solution may use sensors to monitor crop fields and also control irrigation systems utilizing sprinklers or drip irrigation techniques. The farm may be observed from any location. This enables data-driven actions to be taken. For example, if the IoT sensors detect a decline in soil moisture level, the farmer may manually deploy sprinklers or even have software for artificial intelligence in agriculture that executes this step without the farmer’s supervision. This is a much better and more effective method of farming.

4. Artificial Intelligence

The use of Artificial intelligence in agriculture is a new operation method in modern-day agriculture. AI-powered equipment and technology have elevated today’s agricultural system to new heights. This technology has boosted agricultural productivity and real-time monitoring, harvesting, and processing. The most recent automated system technologies, such as agricultural robots and drones, have made a significant contribution to the agro-based industry. Various high-tech computer-based methods are being developed to identify critical characteristics such as weed identification, yield detection, and crop quality.

The Impact of AI on agriculture

AI-based solutions serve to enhance efficiency in all domains and handle the issues encountered by numerous businesses, including the agricultural sector, such as crop yield, irrigation, soil content sensing, crop monitoring, weeding, and crop establishment. Agricultural robots are being developed to provide high-value AI applications in the aforementioned field. The agriculture industry is in trouble as the world population grows, but AI can provide a much-needed answer. The use of artificial intelligence in agriculture solutions has helped farmers to generate more output with less input while also improving output quality and assuring a speedier go-to-market for harvested crops. Farmers will use 75 million linked devices by 2020. Here are some examples of how AI helped farmers:

Image perception and recognition

There is growing interest in autonomous UAVs and their applications such as identification and surveillance, human body detection and geo-location, search and rescue, and forest fire detection. Drones or UAVs are becoming increasingly popular for reaching great heights and distances and carrying out a variety of applications due to their versatility and amazing imaging technology. They also have the ability to be piloted with a remote controller which enables us to do a lot with these devices.

Skills and workforce

Artificial intelligence enables farmers to collect vast amounts of data from public websites and the government. Then it can be used to evaluate it, and present farmers with solutions to many complex concerns. It also enables us to use smarter irrigation, resulting in improved output for farmers. Soon, farming will be discovered to be a combination of technology and biological talents, which will not only provide a better end in terms of quality for all farmers but will also reduce their losses and workloads.

According to the UN, by 2050, two-thirds of the world’s population would be living in cities, necessitating a reduction in the load on farmers. AI in agriculture may be used to automate many operations, decrease risks, and give farmers relatively simple and effective farming.

Maximize the crop yields

Emerging technologies have aided in crop selection and even enhanced the selection of hybrid seed options that are most suited to farmer demands. It has been implemented by studying how the seeds react to varied weather conditions and soil kinds. Plant diseases can be reduced by gathering this information. We can now match market trends, yearly outcomes, and customer requirements, allowing farmers to optimize agricultural returns more effectively.

5. Drones & UAVs

Drones and UAVs are a combination. These are physical objects and aircraft that can be operated without a pilot on board. The drones and UAVs can be controlled from a remote by the operator, or completely by software by using artificial intelligence in agriculture as well.

Aerial drones

Farmers may utilize drones to determine crop health, the presence of weeds, biomass, and water saturation levels on different parts of the field. When compared to satellites, these enable the transmission of the best photos as they are closer. Because these drones are operated locally, they deliver information more quickly and with better quality images. These drones are also used to spray bug repellents and fertilizers. All of these advantages are supplied while reducing farmers’ direct exposure to chemical toxicity and fertilizers, spraying water on the crops, and for livestock farmers, saving the livestock from attackers by spotting them from a distance. 

Another real-life example is the Hornet drone. The Hornet drone inspects crop health from above. This flying bot developed by the robotics business Agribotix functions similarly to a standard drone in that it can fly hundreds of feet in the air at speeds of up to 33 mph. The Hornet assists farmers in getting a birds-eye perspective of their crops. It uses infrared sensors to take overhead photographs and movies of fields and assess crop health. If a specific area of the field looks to have a problem, the bot will notify farmers via an app.

6. AgRobots: Replacing Laborers With Agricultural Robots

AgRobots are also becoming widely adopted thanks to the advancement in robotics and software technologies. Robots are replacing physical labor due to their higher efficiency, cost-effectiveness, and easy management. AgRobots are smart and can detect events even before the experienced farmer. This is because they make use of artificial intelligence in agriculture to produce such bewildering results. Let’s discuss some real-life robots and the companies that founded them.

Prospera

When a farm worker discovers that crops are failing, it is frequently too late. Caterpillars or viruses ate the tomatoes and spinach, and the crops were destroyed. Prospera, a revolutionary robotic system, detects intruders and recognizes when crops are ill by using a network of cameras and sensors. It then warns farmers and informs them of the situation via an app. Prospera, which was founded in 2014, currently works with some of the world’s greatest vegetable producers, including those that supply Walmart and the British supermarket chain Tesco. It is also growing into orchards and vineyards ranging from 50 to 4,000 acres in size.

Bosch’s BoniRob

Bosch, widely renowned for its blenders and power drills, has developed a robot that can eradicate weeds quicker than any person or chemical. The BoniRob roams around fields, knocking out two weeds each second with a 1-centimeter-wide drill. Weeds will not overwhelm the crops this way.

Wall-ye created by Christophe Millot

Christophe Millot, a French inventor, developed the Wall-Ye, an autonomous bot using artificial intelligence in agriculture. It aids in the pruning and harvesting of grapes in vineyards. It can locate and snip weak vines while also monitoring the health of the soil and grapes using infrared sensors and scissor-hands.

Abundant Robotics Inc

Abundant Robotics Inc. is a firm that is creating robots that pick apples when they are ripe. It finds the apples and harvests them swiftly and effectively using computer vision. According to TechCrunch, the bots were intended to remove one fruit each second while causing no damage to the fruit or tree. For instance, a built-in tube vacuums up the apples without damaging the other fruit or the tree. The system is programmed using artificial intelligence in agriculture with a map of the vineyard before it can begin operating, so it knows where to go.

Lettuce Bot by Blue River

Modern Farmer compares the Lettuce Bot to a “Roomba for Weeds.” Blue River, a California-based business, created the gadget, which attaches to a tractor. It can identify insects and weeds using sensors and spray insecticides just in certain regions. Blue River intends to alter the Lettuce Bot in the future to destroy weeds without using chemicals, maybe with a spinning blade. Furthermore, it can thin lettuce fields by destroying a section of the plants to allow the remaining plants to thrive. Blue River claims that it can cure 5,000 plants per minute.

Rover

The Rover, created by Sydney University’s Center for Field Robotics, is intended to herd cattle. Although it is still in the testing phase — and the prototype is still being managed by a person for the time being — cows in trials have responded positively, according to the BBC. The robot tracks where cows are and where they need to go using sensors, cameras, and GPS technology. It goes at a steady cow speed (a few miles per hour), so they are not harried or shocked. Rover is now guided by a human, but the researchers intend to automate it. A future version might potentially gather data on soil quality, inspect agricultural fences, and inspect water troughs.

These examples just show how much potential artificial intelligence in agriculture holds. These systems once perfected become very useful to farmers and can redefine the way we perform farming.

7. Controlled Environment Agriculture

Controlled environment agriculture (CEA) refers to several farming approaches that use predictive analysis software technology. CEA might range from modest shade structures and hoop houses to full-fledged indoor or vertical farms. Fully automated, closed-loop systems with regulated lighting, water, and ventilation are the most sophisticated. CEA also includes conventional methods like using plastic film over field-grown crops, using nets or shade structures, and using aquaponics systems, which combine fish or aquaculture with plant production.

Controlled environment agriculture systems are intended to offer ideal agricultural growth conditions while preventing disease and insect damage. Crops can be cultivated hydroponically, where roots are bathed in nutrient-dense water, or aeroponically, where roots are misted with water and nutrients regularly, in indoor systems with artificial illumination.

Why is CEA important?

A robust food system must incorporate agriculture of all sizes and kinds. As climate change threatens traditional agricultural production and consumers increasingly seek distinctive goods, Controlled Environment Agriculture may play a vital role in ensuring a healthy and nutritious food supply throughout the world. CEA can produce high-quality food close to customers while utilizing less water and other resources. Many of the fresh tomatoes, herbs, and leafy greens we consume are already cultivated in controlled conditions ranging from shade structures to greenhouses. Greens produced entirely under lights in enclosed indoor systems are also becoming more widely accessible in the market.

In the future, CEA will most likely be a key adjunct to more traditional outdoor growing methods. Controlled environment agriculture may reduce inputs like water, fertilizers, and pesticides while simultaneously lowering the risk of food-borne infections and saving money on labor. CEA systems may also be installed in urban locations that are unsuitable for traditional agriculture, bringing food production closer to customers while utilizing available space.

FAQs:

What is the future of agritech?

Agricultural enterprises can no longer afford to rely on gut instinct, human-centered procedures, and technologies from prior eras. The future is software and those who embrace artificial intelligence in agriculture and the use of predictive analytics software and then apply these breakthroughs to their agricultural practices now will be the winners of tomorrow. The future of Agritech is software utilization, robots working in the field, and farmers managing their farms from a simple mobile application. 

Why innovation is important in agriculture?

According to the UN, by 2050, two-thirds of the world’s population would be living in cities, necessitating a reduction in the load on farmers. AI in agriculture may be used to automate many operations, decrease risks, and give farmers relatively simple and effective farming. Agriculture producers may benefit from innovation by increasing production while better managing natural resources. This helps to assure long-term profitability and reduces production’s negative environmental consequences, such as pollution and trash.

What is the future of smart farming?

Based on a more accurate and resource-efficient approach, smart farming has the potential to produce a more productive and sustainable type of agricultural production. Smart farms can be expected to bring the complete solution of feeding humanity. Smart farming can be used to feed our growing population which research expects to reach 9.6 billion by the year 2050.