Food security is one of the priorities of every country in the World. However, different factors are making it difficult to meet global targets on food security. Some unprecedented shocks are encumbering food security at the global level. Various interventions have been applied toward food security and artificial intelligence is one of the modern methods that is being used in various stages of the food system.
From automated pest and plant disease detection to intelligent spraying and produce sorting—here's how Computer Vision is transforming the food and agriculture sector.
source: https://www.v7labs.com/blog/ai-in-agriculture
Article Artificial intelligence applications in the agrifood sectors
There are various recent applications of AI being utilized to develop predictive and preventive modeling tools in the field of Agriculture and Environmental studies. Some examples include crop monitoring and yield prediction through the analysis of satellite imagery and other data sources, precision agriculture utilizing data from sensors and drones to optimize farming practices, and soil health monitoring by analyzing data from soil sensors. AI can also be used to build predictive models for climate patterns, monitor wildlife populations, and analyze data from sensors to detect pollution and identify sources of contamination. Overall, AI-based tools offer significant potential to improve our understanding of agricultural and environmental systems and develop more sustainable and effective management practices.
Hey Eduard Babulak ,
That's a great research and development topic. Over the past few years there have been several methods of implementation of systems to enhance and monitor agricultural aspects for higher efficiency of crop yields.
In terms of Food Security, there have been research and model implementations on irrigation systems (specifically drip irrigations) so as to monitor the water levels of the crop without any wastage or overuse that might harm the crop. Also there have been irrigation systems and networks designed to make water reach efficiently in all parts of the field (here, talking about an irregular patch of a field, an example of an intensive farming with crop rotation).
Majorly IoT (Internet of Things) and embedded systems with the inclusion of AI monitoring. The methodologies described by Ahshanul Haque are the techniques which inculcate both sensor-based monitoring systems and then AI modelling and predictive analysis on the sensor results and checking crop behavior and growth w.r.t the conditions and environment factors.
This shall be helping in performing iterations and changes in the farming methods only at the necessary levels, rather than hit and trial methods and having the undesirable yields.
For e.g.: this research work explains an overview of an ANN model implementation for crop yield.
(PDF) INTELLIGENT IRRIGATION SYSTEM USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING: A COMPREHENSIVE REVIEW (researchgate.net)
There are many recent applications of AI in building predictive and preventive modeling tools in the field of agriculture and environmental studies. Here are some examples:
Overall, AI has the potential to revolutionize the agriculture and environmental sectors by providing more accurate and timely information to farmers and policymakers, enabling them to make better decisions that benefit both the environment and human well-being.
Overall, AI is proving to be a valuable tool for building predictive and preventive modeling tools in the field of agriculture and environmental studies, helping to improve crop yields, mitigate the impacts of climate change, and conserve biodiversity.
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