Dear Rk Naresh,

Agriculture can be made more sustainable with Artificial Intelligence. A broad way in this direction is the use of IoT with Digital and Adaptive Twins of relevant agricultural processes/system components. Machine Learning and Artificial Intelligence serve as the basis for setting up and using such Adaptive Digital Twins.

For the idea of digital twins see: https://www.researchgate.net/publication/366167747_Digital_Twins_in_IoT

In the appendix I have added some literature sources on the use of digital twins in agriculture.

Best regards

Anatol Badach

Mandana Moshrefzadeh, Thomas Machl, David Gackstetter, et al.: Towards a Distributed Digital Twin of the Agricultural Landscape; Journal of Digital Landscape Architecture, 2020, Issue 5; DOI:10.14627/537690019

https://gispoint.de/fileadmin/user_upload/paper_gis_open/DLA_2020/537690019.pdf

Cor Verdouw, Bedir Tekinerdogan, Adrie J. M. Beulens, Sjaak Wolfert: Digital twins in smart farming; Agricultural Systems, Vol. 189(15), Apr 2021; DOI: 10.1016/j.agsy.2020.103046

https://www.researchgate.net/publication/348862772

Christos Pylianidis, Sjoukje Osinga, Ioannis N. Athanasiadis: Introducing digital twins to agriculture; Computers and Electronics in Agriculture, Vol. 184, Mar 2021; DOI: 10.1016/j.compag.2020.105942

https://doi.org/10.1016/j.compag.2020.105942

Pelin Angin, Mohammad Hossein Anisi, Furkan Göksel, Ceren Gürsoy, Asaf Büyükgülcü: „AgriLoRa: A Digital Twin Framework for Smart Agriculture“; Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications; Vol. 11(4), Dec 2020, DOI:10.22667/JOWUA.2020.12.31.077

http://isyou.info/jowua/papers/jowua-v11n4-6.pdf

Suresh Neethirajan, Bas Kemp: Digital Twins in Livestock Farming; Animals, Vol. 11 Issue 4, Apr 2021, DOI: 10.3390/ani11041008

https://doi.org/10.3390/ani11041008

Dr Anatol Badach thank you for your contribution to the discussion

In fact, AI enables farmers to cultivate in a smart and sustainable manner, reaping higher and better yields while using fewer resources. If adopted at scale, AI definitely has the potential to power the next agricultural revolution. For this, farmers, governments and AI experts need to work in tandem to ease AI adoption. By collecting data on plant growth, AI can help produce crops that are less prone to disease and better adapted to weather conditions. With the help of AI, scientists can identify the best-performing plant varieties and crossbreed them to create even better hybrids. AI can help detect field boundaries and bodies of water to enable sustainable farming practices, improve crop yields, and support India's 1.4 billion people and the rest of the world. AI can be appropriate and efficacious in agriculture sector as it optimizes the resource use and efficiency and solves the scarcity of resources and labor to a large extent. Artificial intelligence can be technological revolution and boom in agriculture to feed the increasing amount of human population in the world. AI systems are helping improve the harvest quality and accuracy, which is known as precision agriculture. AI technology assist's in detecting the diseases in plants, pests, poor plant nutrition, etc. It also allows the farmers to monitor the health of the crops and the soil.PwC numbers estimate that the use of AI can reduce worldwide greenhouse gas (GHG) emissions by 4%. Use of AI for environmental purposes can also contribute up to $5.2 trillion USD to the global economy in 2030. Artificial intelligence (AI) is playing a critical role in the agriculture sector as it helps farmers to increase crop yields, improve crop quality, and reduce production costs. In addition, AI can help farmers to optimize irrigation systems, predict weather patterns, and forecast market prices.IoT and AI based systems are capable of enhancing input use efficiency on the farm. Smart farming leverages digital technologies to automate agricultural operations in real-time. Deep learning based solutions can solve numerous day to day agricultural problems. By collecting IoT data, smart sensors can enable real-time monitoring of “what is happening on the ground.” Farming can be made more efficient by knowing when to harvest, the amount of water used and whether irrigation is needed, soil health, and fertilizer requirements. I oT in agriculture uses robots, drones, remote sensors, and computer imaging combined with continuously progressing machine learning and analytical tools for monitoring crops, surveying, and mapping the fields, and provide data to farmers for rational farm management plans to save both time and money. Using the deep learning and ML techniques, the crop productivity in the agriculture can be improvised. By using the sensor data, the accurate data are predicted by evolving the artificial intelligence, which allows a smart way in farmer decision making. Using the deep learning and ML techniques, the crop productivity in the agriculture can be improvised. By using the sensor data, the accurate data are predicted by evolving the artificial intelligence, which allows a smart way in farmer decision making.