Technology, sustainability, agriculture, farming, and food are prominent topics in California.
Agriculture is one of the industries making substantial progress in leveraging artificial intelligence, machine learning, deep learning, neural networks, IoT, and drone technologies. For example, streaming data from IoT systems feed AI systems for descriptive, prognostic, and predictive analytics for farmers.
The agricultural industry is critical for our survival. Essentially, humans need food, and agriculture is the primary source of it. Unfortunately, even though agriculture has developed significantly, humanity still experiences hunger and faces food insecurities.
According to the United Nations (Department of Economic and Social Affairs), more than 690 million people still go hungry. Moreover, food insecurities are on the rise. Therefore, in addition to hunger, malnutrition and undernutrition are critical concerns for many global populations.
Unfortunately, the recent pandemic created an additional threat to food systems. According to the UN, the four critical points affecting food insecurity are Covid-19, the locus crisis, climate shocks, and conflicts.
Sadly, United Nations reports highlight that 144 million children under five years of age are stunted. And 47 million of them are affected by wasting.
On the one hand, we have severe obesity concerns in developed countries, and on the other hand, chronic hunger prevails in underdeveloped countries. Ironically, those who experience obesity issues also suffer from micronutrient deficiencies.
Can smart agriculture and farming can address these debilitating issues?
As a technologist, I can see the potential in the emergence of digital agriculture and cognitive farming. From my reviews of the literature, these emerging technology constructs can contribute to resilient agriculture for sustainability.
For example, food wastage is one of the most significant issues in agriculture. Ingredients of food are produced on farms, but they are wasted in processing and distribution. Growing, processing, preserving, and distributing food ingredients are the prominent use cases of digital agriculture and cognitive farming. Emerging technology stacks can create innovative solutions to address the issues related to these particular points.
While agriculture and farming are critical aspects of producing food, many other angles are spanning to different industries and sectors, including preservation, distribution, education, health, and pricing.
In this article, I want to introduce digital agriculture and cognitive farming concepts and how California is leading these fields.
According to McKinsey, the global food and agriculture business is around $5 trillion. According to Statista, in 2020, there were over two million farms in the US. However, the number of farms has been steadily dropping since 2007, when there were about 2.2 million farms in the United States.
Agriculture is closely related to climate. Climate change is a globally discussed topic among scientists. Climate is a complex discipline requiring both human and artificial intelligence Scientists give glimpses to the future by using historical and current big data empowered by AI, ML, and particularly predictive analytics methods forecasting future outcomes based on these complex data sets.
This recent scientific podcast with Professor Will Stefan reflect the tipping points, shows us how bad the current situation is and the difficulty of modelling climate systems. In addition, the podcast provides insights into the threat of social collapse due to climate change and why we really need to get our act together by 2030 to try and stabilize the climate system.
Furthermore, climate changes affect agriculture which consequently affects food security. This comprehensive UC Berkeley study indicates climate change issues focusing on food and agriculture, population and security up to 2050.
The emergence of Digital Agriculture and Cognitive Farming
AI technologies are expected to grow substantially. According to Marketsandmarkets report, AI opportunities will grow four times by 2026. In their report, some of the top players are John Deere, Farmers Edge Inc, AgEagle, Descartes Labs, IBM, and Microsoft.
Digital agriculture and its subfield cognitive farming are novel approaches for sustainability. A recent scientific paper related to advances in computational and bio-engineering, published in Springer, defines cognitive agriculture as basic principles for producing agricultural products that are environment friendly, resource-saving and as well as highly efficient.
Cognitive farming is an emerging subdiscipline of digital agriculture, including AI and IoT enabled farms. The cognitive farm aims to create farming models and smart farms with self-learning and self-managing capabilities.
The key enablers of cognitive farms are robotics, automated AI/ML models, big data, IoT generated streaming data and emerging equipment such as drones.
Agriculture has come a long way over the millennia in how we farm and grow crops with the constantly improved various technologies introduced. The industry is now turning to AI technologies to help yield healthier crops, control pests, monitor soil and growing conditions, help reduce the workloads, organize data for farmers, and improve a wide range of agriculture-related tasks in the entire food supply chain.
Forbes published an article about how AI is transforming agriculture. Kathleen Watch and contributing group Cognitive Word highlight that with the help of AI, farmers can now analyze a variety of things in real-time such as weather conditions, temperature, water usage or soil conditions collected from their farm to better inform their decisions. For example, AI help farmers optimize planning to generate better products by determining crop choices.
This article also points out the importance of computer vision and deep learning algorithms in processing data captured from drones flying over extensive fields covering more land in much less time than humans. For example, AI-enabled cameras can capture images of large farms through drones and analyze the captured images in near-real-time. Thus, these tools can identify problem areas and potential improvements.
Drones are not just toys and entertainment tools anymore. They are used in many industries like mining, telecommunications, security, infrastructure, transport and agriculture. For example, according to consulting firm PwC (PricewaterhouseCoopers), the value of drones for agriculture is $32.4 billion. Drones using 3-D mapping techniques can identify and factor in soil viability, drainage, and irrigation. Furthermore, they enable aerial spraying and crop monitoring in cognitive farms.
Mindtree published a comprehensive paper covering the role of AI in agriculture. The report is publicly available in this PDF document. The paper covers five major technology topics for intelligent farms. They are growth driven by IoT, image-based insight generations, identification of optimal mix for agronomic products, health monitoring of crops, and automation techniques in irrigation. AI enables precision farm management. They can even identify the stress levels in plants using computer vision, IoT sensors, and predictive analytics.
There are many agricultural matters specifically resolved by artificial intelligence. Some common AI activities are weed control, harvesting, packaging, pest management, soil defects, crop health, lettuce thinning, disease prediction, and self-driving tractors.
Several outstanding AI ventures are producing innovative solutions in the digital agriculture field. The popular ones are Trace Genomics, Blue River Technology, aWhere, SkySquirrel Technologies, Harvest CROO Robotics, and PEAT.
Large agriculture business organizations and farms are already using these expensive technologies. However, the cost of cognitive farms is prohibitive for smaller businesses. Mass production of cognitive tools, generation of new ideas by communities, and use of open-source applications may reduce the cost and might make it available for small business owners. Several companies are making substantial progress in California and nationwide.
According to J. Mark Munoz (Professor of Management at Millikin University) in California Management Review, while cognitive farming challenges exist, the evidence supporting its case is compelling. The future of farming and agriculture is in the hands of farm owners and managers worldwide. In a cognitive farm with a digital farm manager, a likely response to the question is - How green do you want it to be?
Several large farms are already using cognitive farming practices in California. For example, this 2-acre vertical farm is managed by AI and Robots and uses 99% less land. Here is a short YouTube video showing what a cognitive farm looks like now. There will be more of those innovative farms in California.
Despite technological advances in farming and agriculture, California faces serious issues related to food. Global Citizen channel on YouTube mentions that California is by far and wide the largest producer of food in the US.
The state produces billions of pounds of food every year, and yet, somehow, 1 in 8 Californians struggle with food insecurity. More specifically, Los Angeles County, the most densely populated county in the state, has the largest food insecure population in the country.
I believe these technological advances, their smart applications, and integration to agriculture, farms, and the overall food industry by Californians can address these known issues and reduce risks for food insecurities in the state and nationwide as mentioned in the Global Citizen channel.
Thank you for reading my perspectives.
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