AI contributes to solving food waste.

Since the 18th National Congress of the Communist Party of China, various departments in various regions have made great efforts to rectify the phenomenon of extravagant waste, however, the phenomenon of food and beverage waste still exists. It is reported that China wastes 80 billion to 100 billion pounds of food and beverage each year, equivalent to 6.0% to 7.5% of the current food production. In addition to the waste on the table, China's post-natal loss of grain is also quite serious, the annual loss of more than 70 billion pounds. The application of artificial intelligence technology (AI) offers more possibilities to solve the problem of food waste in the whole industry chain from farm, processing, logistics to consumption.
AI is widely used
in agriculture, and unmanned farm machines work in wheat fields at sunset, harvesting wheat in an orderly manner. Such images used to seem to exist only in sci-fi movies, but now, driven by artificial intelligence, the Industrial Internet of Things, big data, 5G and other technologies, AI, once a sci-fi work, has become a reality.
In fact, as early as more than a decade ago, AI has been used in agriculture, from early warning of climate disasters, soil pest detection, to drone seeding, farming, harvesting in recent years, AI is moving into the field at an unexpected rate. With the large-scale landing of AI in agriculture, the problems of rural labor shortage in the future may be effectively solved.
Artificial wisdom into agriculture, can be applied data analysis, statistical modules, automatic robots for more efficient farming, artificial wisdom can also be applied to algorithmic images and data to accurate food processing, to solve the problem of labor shortage and labor wages, the market side of the market can use traceability system to restaurant, retail and other demand series, food waste data tracking, classification, and then produce a new state of food supply chain, curb excessive production, inventory and waste.
fact, the agricultural circular economy has slowly taken root in history, aiming to reduce waste and pollution and make products, raw materials and recycled natural systems sustainable. However, in the agricultural circular economy, the consumption of limited resources is different from the pace of growth, and AI can shorten the transition period for the agricultural circular economy to overcome these problems.
to improve farming methods and efficiency
.AI can help farmers avoid costly and time-consuming field trials by identifying new ways to best regenerate agriculture through information analysis. For example, the use of data analysis, statistical modules, and AI to simulate field trials and ambient ecosystems under different conditions allows you to explore possible outcomes while avoiding the risk of environmental damage or sacrificing yields so that they can learn and improve profitability and increase sustainability.
combination of AI algorithms with robotics can further automate and improve the ability to control farming processes, such as AI, which can be used to analyze crop images to help farmers decide when to harvest. In addition, harvesting can also be done by automated robots. This reduces waste of food in the field and allows for more accurate production forecasts by improving information in the supply chain and maximizing the efficiency of storage environments and preservation equipment.
AI can also accurately identify and recommend correct prevention methods for agricultural products, creating "doctors" who can see crops, improving the quality of agricultural products, reducing the overuse of pesticides, and increasing overall crop yields. AI is combined with pests and diseases, a data set of pests and diseases is established, and through the collaboration of learning and image recognition system technology, AI monitoring adopts a special computer algorithm model, identifies by spectral signals of pest and disease, and realizes real-time identification and identification of pest and disease conditions according to effective data characteristics.
reduce food waste from multiple
.Ai algorithms can use images and data generated from cameras, X-rays, and near-infrared spectra to automatically classify different products during food processing, such as picking carrots and potatoes based on their best use, size, shape, and quality, reducing time-consuming, expensive, and inaccurate problems due to manual screening.
some companies use AI traceability and dynamic pricing to help supermarkets and other retailers sell food for the duration of the year. Some institutions and restaurants use new tools to obtain, track and classify data on food waste. Moreover, the algorithm can predict sales, allowing restaurants, retailers and hotels to more effectively connect supply and demand.
Japanese company has developed an artificial intelligence system to detect bones that are difficult to remove in chicken. X-rays used in the original inspection system sometimes gave incorrect results, causing the meat to be thrown away rather than processed into fried chicken and other products. The company hopes to reduce food waste in chicken processing by 80% within three years.
MIT's Sensory City Laboratory and Alp Labs are working on a prototype intelligent wastewater treatment platform that uses AI to combine physical infrastructure and bio-chemical testing technology to explore pathogens in human wastewater, which could eventually help reuse wastewater into recycled food systems.
AI plays an important role in the transition period in agriculture and recycled food systems, changing the way food is grown, harvested, distributed and enjoyed.
as more and more data sources are available for reference, and as the ability to calculate increases, AI in the future can more effectively help distribute food supply and demand, improve supply chain efficiency, and curb overprodulation, overstocking, and waste.
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