such as crop temperature , vegetation levels , and soil quality , while the machinery responds to this information by adjusting seed , nutrients , herbicides , and fertiliser . |
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12 . Blockchain A blockchain is a ledger of records that enables transparent , secure , and realtime information sharing within a business network . Each “ block ” of data is secured with cryptography to minimise the risk of manipulation or censorship .
Blockchain solutions manage various types of data , including online transactions , currencies , medical records , shipping documents , and production chains .
In the agricultural sector , in which a myriad of valuable transactions occur every day , the technology has numerous applications . For example , increased data transparency exposes supply chain vulnerabilities , which enables growers , buyers , retailers , and consumers to implement robust risk mitigation strategies .
Blockchain solutions reduce the occurrence of fake food labelling , provide accurate information surrounding the cost of production , and guarantee expected product quality . These use cases are of particular benefit to small farms , which suddenly have the means to attract large buyers , such as grocery chains .
Finally , smart contracts stored in a blockchain offer a new level of security to suppliers and buyers .
13 . Internet of Things ( IoT ) IoT refers to the collective network of physical objects embedded with sensors and software to facilitate inter-device and cloud communication .
IoT devices are embedded in soil , water tanks , crops , machinery , fields , vehicles , greenhouses , drones , and livestock in agriculture . They monitor temperature , humidity , soil moisture and fertility , inventory , weather conditions , nutrient levels , and animal location and behaviour .
As a result , farmers gain a comprehensive understanding of their crops ’ behaviour and growth , enabling them to pursue optimal strategies to increase crop yield and quality . In addition , the implementation of IoT devices can help farmers improve efficiency , streamline food supply chain operations , raise healthier livestock , optimise resources , and minimise labour shortages .
The global IoT in agriculture market size was valued at US $ 27.1 billion in 2021 and is projected to reach US $ 84.5 billion by 2031 .
14 . Robotics Agricultural robots augment the role of
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farm workers by assuming slow , repetitive , and labour-intensive tasks .
Some of the most common agricultural robots include :
• Aerial Imaging Robots – to inspect crops from the air .
• Seeding and Spraying Robots – to deploy seeds , fertilisers , and pesticides in optimum locations .
• Harvesting Robots – to carefully harvest crops and fruits .
• Autonomous Mobile Robots – to transport fruits , vegetables , and plants around the farm .
• Weeding Robots – to identify , kill , and remove weeds .
• Robotic Greenhouses – to plant , prune , and harvest produce . The use of robots like these enabled farmers to focus on value-add activities , enhance productivity , use resources more efficiently , lower food production costs , and improve worker conditions .
15 . Artificial Intelligence ( AI ) / Machine
Learning ( ML ) & Data Science AI , ML , and data science have numerous applications in agriculture , serving to improve agricultural productivity , facilitate data-driven decision-making , lower costs , and drive sustainable business practices .
A solution that analyses soil conditions , for example , can determine crop health , identify lacking nutrients , spot pests and diseases , and predict yields . These insights enable farmers to adjust
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their methods to optimise output and quickly address potential issues .
Other AI-powered tools can detect leaks in irrigation systems , monitor the impact of livestock diets to improve well-being , complete yield mapping on large datasets , predict the best time to harvest crops and identify the plants most resilient to extreme weather .
Some of the biggest challenges associated with the adoption of AI and ML solutions in agriculture are high upfront costs , a lengthy technology adoption process , and industry scepticism .
16 . Regenerative Agriculture Used by Indigenous communities for centuries , regenerative agriculture takes a holistic approach to food and farming systems , centring conservation and rehabilitation .
The practice is designed to restore soil health , address food inequity , and leave the planet in better shape for future generations . Industrial-scale agricultural practices have a huge impact on the environment .
The core principles of regenerative agriculture are nurturing relationships across ecosystems , prioritising soil health , reducing the use of inputs such as herbicides , pesticides , and fertilisers , and promoting ethical working environments .
Cover crops and green manure , crop rotation , and crop diversification are essential components of regenerative agriculture .
17 . Controlled Environment Agriculture ( CEA ) Given the world ’ s volatile climate , CEA is one of the most critical innovations in agriculture . It refers to the production of plants and their products within a controlled environment , such as greenhouses or vertical farms , to optimise productivity .
Not only does CEA enable produce to be grown in any place at any time , but it also protects against harmful contaminants , such as pests and pollution . Typically , less land and water are required and farms are located within close proximity to the end customer , which reduces food miles and wastage .
18 . Drones The most common agriculture drones are
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single-rotor or multi-rotor drones . Singlerotor drones are usually fairly large , which means they can manage heavier payloads , while multi-rotor drones are compact , easy to control , and can hover with ease .
Drones are typically integrated with precision agriculture devices to scout pests and diseases in crops , monitor water quality , spot stray livestock , provide high-resolution data for large land areas , and collect samples .
The global agricultural drone market size was valued at US $ 1,176 million in 2022 and is expected to reach US $ 6,836 million in 2028 .
19 . Biotechnology According to the US Department of Agriculture ( USDA ), agricultural biotechnology describes “ the range of tools that alter living organisms , or parts of organisms , to make or modify products ; improve plants or animals ; or develop microorganisms for specific agricultural uses .”
Scientists in this sector have made exciting advancements in recent years . For example , it ’ s possible to engineer crops to tolerate specific herbicides , which enables farmers to better manage weeds , or make them resistant to certain diseases and pests , which helps with pest control .
In the future , agricultural biotechnology could result in nutritionally enriched or longer-lasting produce or aid in the development of new medicines .
20 . Big Data & Analytics Farmers can use big data and analytics to meaningfully interpret information collected by various agricultural technologies , which ultimately informs data-driven decision-making .
For example , precision agriculture sees data being collected via sensors , drones , robots , and autonomous machinery . Comprehensively collating and reviewing this data could highlight problems such as the presence of pests and diseases or non-optimal soil conditions . It can also be used to monitor market trends , predict weather patterns , and optimise the workforce . – @ AGROBiz
This is an excerpt from an article which appeared on the website of Thomas , North America ’ s number one industrial sourcing platform and marketing powerhouse .
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