Robotics in Agriculture


Introduction- For agriculture to thrive disruptive technologies and innovations must fall into place. Farmers constantly seek new ways to improve the Yield per acre, and at the same time, deal with the rising input costs.

We will take a look in this blog at the most innovative advances and emerging robotic technology in the agriculture sector, and explore their impact on the farmers.

1. Technology backing advanced robotics in agriculture : A lot of technological advances in recent years have led to the development of robotics in the sector of agriculture. Robotic advancements use machine vision technology.

Machine Vision Technology is very useful to avoid hazards, identify crops, and even determine their readiness towards harvesting. Machine vision involves multiple cameras feeding information to the robot that allows it to locate and access the crops around it. Robots perform tasks like weed picking, growth monitoring, harvesting, sorting and Packing.

Robotic farm equipment use the GPS technology to gain the exact position and locate themselves precisely on the farms. Autonomous field ploughing, seeding, navigating tractors; a combination of computer vision sensors and GPS is integrated and acts as the driver in robotic ploughing trucks.

Machine Learning is also widely employed in agricultural robots. ML provides an advanced method of identifying collision paths. It helps the robots/autonomous vehicles to adapt & avoid new or unexpected hazards in their paths. It also helps in picking & quality control of the robots as they go under training.

2. Robots in the Field: Autonomous Tractors :
Self Driving : In the case of autonomous farm equipment, machine vision and movement sensors work hand in hand to avoid obstacles while navigating the field. The robots create a virtual 3D model of the surface, and with the help of high-resolution cameras, they’re able to navigate freely. Movement is, however, not automatic as it depends on what parameters they are programmed to avoid or maneuver through.

Seeding : Autonomous precision seeding is a relatively new technology that combines robotics and GIS mapping. By generating a map of a field and including information about soil properties such as density and soil quality, autonomous seeders are able to seed more accurately than traditional broadcast spreading seeders or drone seeders.

Weeding : Using computer vision and a variety of mechanical tools, the robot plucks out individual weeds instead of using chemicals.

Spraying : Smart sprayers are typically paired with computer vision cameras to identify weeds for targeted herbicide applications. Sophisticated systems can even identify specific plants and activate only the relevant application nozzles. This means less waste, reduced herbicide resistance and more efficient application across fields.

3. Robotic Harvesting :
The robotic system utilizes soft-touch robotics and a lidar sensing system to detect ripe apples, leaving out unripe fruits during the picking process. Innovations in so-called “soft” harvesting, where machines are equipped with delicate suction cups or padded grabbers are just one way growers are protecting their fruit during autonomous harvests.

Harvest Quality Vision (HQV) is an exclusive technology that allows growers to scan a bin of apples with a camera attachment, which creates a 3D model of the scanned fruit. From these scans, HQV analyzes the samples to determine the size, color profile, and quantity of apples scanned in just moments. HQV can replace traditional pre-sorting by sizing harvested fruit right in the bin, reducing handling and transport that can damage the fruit.

Vision Robotics has also developed a similar system that involves a pair of agri-bots that work hand in hand. One robot uses 3D technology to map the farm area, gathering data that includes the location and size of each orange. The second robot is designed to pick oranges using its unique eight arms, guided by the 3D map given by the first robot to navigate and select the ripe oranges only.

4. Robotics in nontraditional farming :
Robotic Green Houses: One of the agricultural sectors with the most robotic innovations is indoor growing. One of the robots uses sensors as “eyes” to analyze and pick the plants while the other lifts and transports the trays across the facility. All the plants in the farms are grown under specialized LED lights, and the facility claims to use 90% less water than conventional farming.

Agricultural Drones: Flying robots (drones) in farming, which facilitates precision farming during its operations. While drones are not necessarily an innovation, their use in agriculture is beneficial as they can be used in imaging, crop assessment, land monitoring, crop protection, land fertility, among others. It monitors vegetation and successfully discerning unhealthy leaves using the normalized difference vegetation index (NDVI). However, drones fitted with such sensors can gather this kind of data both quickly and efficiently.

Conclusion : Innovation is essential in any sector and currently more relevant in agriculture than ever before. Embracing new technologies like robotics, machine learning, and computer vision will be a key factor in the changing face of agriculture worldwide. It will also reshape the definition of farm workers, easing the workload and at the same time showing promising results in crop efficiency, increased yields, and managed input costs. Agtech is here to stay, and investors are more focused than ever on sustaining our economy through automation, robotics, vertical farming, modern greenhouse practices, artificial intelligence, and precision agriculture.

References:

  • King A. Technology: The Future of Agriculture. Nature, 2017; 544: S21.
  • Wolfert S, Ge L, Verdouw C, Bogaardt M J. Big data in smart farming – A review. Agric. Syst., 2017; 153: 69–80.
  • https:/ / croptracker.com .
  • Oberti R, Marchi M, Tirelli P, Calcante A, Iriti M, Tona E, et al. Selective spraying of grapevines for disease control using a modular agricultural robot. Biosyst. Eng, 2016; 146: 203–215.

Ms. Asha Nama, Assistant Professor, School of Agricultural Sciences, Career Point University, Kota

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