Agriculture plays a vital role in economic growth and development. Over the years, AI-based technological improvements have deeply impacted farming and transformed business. These technologies can help farmers to be proactive rather than reactive in their farming practices. However, to reap all these benefits, effective collaboration between government, science, and business is also crucial. This article attempts to outline the challenges facing Indian agriculture along with the key AI-based smart agriculture technologies, their significance, and potential solutions.
Introduction
The agriculture sector in India is one of the most important in the country’s economy, with a current worth of $370 billion. According to the Food and Agriculture Organization of the United Nations, the population will grow from 7.5 billion to 9.7 billion by 2050, putting further strain on land1. Agricultural producers and enterprises are under tremendous pressure to develop new strategies to increase productivity while reducing expenses and waste. Technological advancements in agriculture depend on Artificial Intelligence (AI)-based technologies, and these technologies could signal a significant paradigm shift in the agriculture sector. AI-based technologies work by interpreting patterns in vast volumes of data and transforming those interpretations into human-like actions. According to McKinsey and the National Association of Software and Service Companies (NASSCOM), an Indian NGO, accessing 15 critical agriculture databases could have a potential of $65 billion in India alone. The AI-based agriculture market was valued at USD 766.41 million in 2020 and is expected to reach USD 2468.02 million by 2026, growing at a CAGR of 21.52 percent. The largest businesses involved in AI-based agriculture technologies include Microsoft Corporation, IBM Corporation, Granular Inc. Game-changers in this space 3. It is depicted that the AI market has a huge potential in the agriculture sector and hence these business houses have planned to invest funds in this sector.
AI-based smart agriculture technologies
There are many smart and sustainable technologies, such as precision agriculture, vertical farming, smart green housing, image processing and agricultural drones/robotics, that can be used in agriculture.
a. Precision Agriculture Precision agriculture is the concept of employing information technologies such as global positioning systems, monitors, remote map-based devices, geographic information systems and targeting systems to collect precious, in-depth data from a wide range of sources, which help in making an accurate and timely decision for better yield. Precision agriculture helps to increase agricultural productivity, reduce the use of chemicals in agricultural production, reduce labor periods, efficient use of water management, distribution of advanced farming methods to improve product quality, quantity, and cost-effectiveness, as well as also help to develop positive perceptions and improve the socio-economic conditions of farmers.
B. Vertical Farming Vertical farming is the urban cultivation of crops within the construction of a metropolis or urban area, with floors built to accommodate specific crops. Vertical farming involves four major components: producing more food per square meter by stacking crops, proper balance of natural and artificial light, using hydroponics or aeroponics instead of soil, and incorporating sustainability features to offset energy costs. There are many advantages to vertical farming, including improved crop yields, reduced water consumption (95 percent less than traditional farming), reduced weather impacts, increased organic farming yields, increased human and environmental safety, and no exposure to heavy farming equipment or disease.
C. Smart Greenhouse Greenhouse agriculture is a potential and alternative approach to future food security and social-ecological sustainability. It involves the use of a house like enclosure made of glass or plastic to protect plants from pests, diseases and other harmful environmental conditions. Cold frame greenhouse farms can be set up to retain heat from the sun and keep the plants warm during cold weather. However, shaded greenhouses are used in dry and hot weather, which helps in maintaining the moisture of the plants. These techniques allow farmers to extend the planting season to grow different crops by modifying local environmental factors such as temperature, light, moisture and nutrients which ultimately produce high quality crops.
D. Image Processing Image processing is a technique that displays the disease detected in the respective plant, as well as the cause of the disease and what method should be used to control the disease. It also displays moisture, humidity, temperature and so on. The source of radiation is essential in image processing. The sources are gamma-ray imaging, X-ray imaging, imaging in the UV band, imaging in the visible band and IR band, imaging in the microwave band, and imaging in the radio band. Image processing can improve decision making in areas such as vegetation measurement, irrigation, fruit sorting, etc. With weed detection, segmentation and classification in fruit grading systems can be accomplished with high accuracy.
E. Agricultural Drones/Robotics Agricultural robots and drones integrate routine and uninteresting tasks for agricultural producers, allowing them to focus on increasing the yield of higher crop production. In agriculture, drones are used for aerial photography, tracking, land auditing, supervision, spraying manure, and to inspect infected or decaying crops. However, some of the most common agricultural robot applications are weed control, harvesting and picking, automatic grass cutting, pruning, sowing seeds, spray coating, sorting, and packing.
Importance of AI-based Smart Agriculture Technologies
The agriculture sector faces various issues such as climate impact, plant disease, improper soil analysis, pest infestation, irrigation, inadequate drainage, and many other issues. But AI-enabled smart agriculture technologies can fulfill the dreams of farmers. Massive structured and unstructured data is generated daily from the Internet of Things (IoT), such as historical weather patterns, soil composition reports, rainfall, pest infestation, crop moisture, temperature in growing areas, and prediction of ideal time for harvesting. All such real-time data collected from various farmers/locations can be sensed by cognitive IoT devices, which can provide valuable insights to increase the yield while reducing costs. AI aids in spraying herbicides only on targeted weed grown areas, which helps in saving money and reducing pollution of the surrounding ecosystem. Hence, AI-enabled smart technologies in agriculture have helped farmers to know the best irrigation and fertilizer treatment timing, water management, crop rotation, timely harvesting, crop type, optimal planting, pest attacks, precise pesticides/herbicides spraying and nutrition management.
Challenges of AI-based Smart Agriculture Technologies
Undoubtedly, AI has redefined the traditional methods to boost efficiency and crop production rate with advanced approaches, and its use can ensure higher productivity. But still, several challenges like availability of IT infrastructure and experts, rural broadband structure, high power cuts, and high cost are the greater barriers to effectively implement AI-based smart agriculture technologies in India. Further cost-benefit analysis for adopting digital farming technology is also a big challenge due to the high cost of these smart devices. For example, an Indian-made drone that can be used for spraying purposes costs around Rs 4 -5 lakh. However, looking at it from a financial perspective, we should also keep sustainability aspects in mind.
Another major challenge is developing a bridge between farmers and data-capture engineers, which will help define the accountability and responsibility of each individual. For example, the responsibility of accurately spraying pesticides/herbicides on crops needs to be defined. If a high number of traces are found after crop harvest, it has a significant impact on consumer health after consumption and leads to high rejection of export consignments by importers, which ultimately leads to many economic losses. Now, another question is who will have the copyright and control over big data and turn it into valuable information. AI systems need to constantly feed new information into the data bases used for effective performance.
Also, machine learning, artificial intelligence, and advanced algorithm design have advanced at breakneck speed, but collecting well-tagged, meaningful agricultural data is still a major challenge. Hence, the responsibility and accountability of all stakeholders needs to be defined to effectively implement these smart technologies in the agricultural sector. Moreover, the misuse of big data is creating additional legal and ethical challenges for regulation and monitoring. Hence, the government needs to take initiatives to establish a regulatory architecture in this sector.
Government Initiatives in this Direction
The government has taken some laudable initiatives as following: 1. It encourages farmers to employ drones by offering them fundamental incentives through the “Sub-Mission on Agricultural Mechanization”. 2. For agriculture initiatives, the Government of India has launched the Digital Agriculture Mission 2021 25. It aims to assist projects that use emerging technologies such as artificial intelligence, blockchain, remote sensing, GIS, and the use of drones and robots. 3. The Union Ministry of Agriculture and Farmers Welfare signed five MoUs (Memorandums of Understanding) with Cisco, Ninjacart, Jio Platforms Ltd., ITC Ltd., and NCDEX e-Markets Ltd. in September 2021. In the third quarter of last year, Cisco released an agriculture digital infrastructure (ADI) solution that improves farming and knowledge exchange.
Conclusion
AI will significantly improve the efficiency of the farming industry. However, we must ensure collaboration between governments, agricultural scientists, IT firms and businesses regarding adequate investment and research. This requires mature reforms considering the expanding population, farmer requirements, operational policies and declining farmland. In addition, research is necessary on a comprehensive framework to evaluate digital agriculture solutions, which includes criteria to determine sustainability, social, economic, ecological, technical, quality and interoperability. To put it another way, Artificial Intelligence (AI) can assist in the development of a strong agricultural economy. The government can potentially promote this through public-private partnerships (PPP).
Read Also:
- How AI Is Revolutionizing Agriculture
- Artificial Intelligence In Agriculture – Paving Way Towards Future Farming
- Application Of Artificial Intelligence (AI) In Agriculture: An Indian Perspective
- Challenges And Opportunities Of Artificial Intelligence In Agriculture
- Artificial Intelligence For Agriculture
- Artificial Intelligence In Agriculture
- Role Of Artificial Intelligence In Indian Agriculture
- Artificial Intelligence (AI) Techniques Used For Diseases Detection In Agriculture
- Study On Artificial Intelligence Applications Uses In Agriculture
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