Text | Xiao Xin said history
Editor | Xiao Xin tells the history
Artificial intelligence has been widely used in agriculture in recent years. To breed healthier crops, manage pests, monitor soil and growing conditions, analyze farmers' data, and enhance other management activities in the food supply chain, the agricultural sector is turning to AI technology.
This makes it difficult for farmers to choose the ideal time to plant seeds, AI can help farmers choose the best seeds for specific weather conditions, and it also provides data on weather forecasts. AI-powered solutions will help farmers produce more with less, improve crop quality, and speed up the time to market.
AI helps to understand soil quality, and AI improves soil quality by helping farmers suggest nutrients they should use. AI can help farmers choose the best time to plant seeds, and smart devices can calculate the spacing between seeds and the maximum planting depth.
An AI system known as the Health Monitoring System provides farmers with information about crop health, as well as nutritional information needed to improve yield, quality and quantity.
Using AI, farmers now have access to advanced data and analytics tools that will promote better agriculture, increase efficiency, and reduce waste in biofuel and food production while minimizing negative environmental impacts.
Artificial intelligence and machine learning have transformed various industries, and the wave of artificial intelligence has now reached the agricultural sector. Companies are developing several technologies to make it easier to monitor farmers' crop and soil health. Hyperspectral imaging and 3D laser scanning are leading AI-based technologies that can help ensure crop health. These AI technologies collect precise data about crop health for analysis.
The article briefly introduces the agricultural development process of artificial intelligence and some agricultural parameters monitored by artificial intelligence. Finally, important applications of AI in agriculture are identified and discussed.
introduce
Agriculture is one of the oldest and most important industries in the world. The world's population is growing rapidly, increasing the demand for food and employment. As a result, new automated methods are being introduced to meet food needs, as traditional methods used by farmers are inadequate to meet these needs, while also providing employment opportunities for billions of people around the world.
Due to labor shortages, strict legislation, an increasing global population, and a dwindling number of farmers, farmers are forced to seek new solutions. Technologies such as the Internet of Things, big data and analytics, artificial intelligence and machine learning have entered almost all industries.
Efforts and research are underway to achieve "connectivity" and "intelligence" through "smart agriculture" to improve the quality and quantity of agricultural products. Spray pesticides on growing areas in the open air or greenhouse environment to increase yields.
Farmers can also use machine learning techniques as part of precision agriculture management, where pesticides are applied based on time, location, and affected crops. Farmers must accurately detect and sort the quality characteristics of crops to increase product prices and reduce waste.
Water management in agriculture has a significant impact on agronomic, climatic and hydrological balances. Applications based on machine learning techniques can estimate evapotranspiration on a daily basis, weekly or monthly, enabling irrigation systems to be used more efficiently.
In addition, accurate daily dew point temperature prediction helps identify expected weather phenomena, as well as estimate evapotranspiration and evaporation. Farmers are increasingly using artificial intelligence and machine learning technology models to improve productivity, with the food technology sector benefiting the most. Robots and sensors are currently being used to manage and monitor crops and collect crop-related data.
There are growing opportunities for machine learning techniques to be used in digital agriculture. Machine learning techniques are a safe way to increase agricultural productivity while reducing environmental impact. By collecting crop data, farmers can better understand crops, their genes, and underlying diseases.
As computing power advances and cloud penetration increases, more parts of the world economy are already benefiting from AI. Agriculture is an area that is already beginning to benefit from artificial intelligence, whether it's weed control, calculating the best time to harvest crops, monitoring soil and crop health, or predicting yields in advance. Over the past decade, AI and machine learning techniques have been tested as development tools in various industries.
However, it has only recently been discovered that AI can be used to improve agricultural decision-making. In particular, farmers may make better decisions thanks to AI technology, resulting in greater efficiency in agriculture and livestock production. Various factors are driving the agricultural sector to want to embrace AI technology in order to make better decisions. It is undeniable that the increase in available data and the ease of access to it are paramount.
In agriculture, monitoring systems based on artificial intelligence and machine learning technology provide insights for monitoring crops, detecting pests, and diagnosing soil failures, allowing farmers to plant seeds at the optimal time for maximum yield. Weeds threaten many agricultural activities, reducing farm yields, invading crops, and suffocating pastures.
AI sensors can detect weed-infested areas and determine the best herbicide to use in that area. AI systems can predict weather patterns, assess crop health, and detect diseases, pests and diseases, or plant nutritional deficiencies. Farmers can monitor the health of their crops through AI drones.
Experts examined images taken by drones and compiled a report on the health of the farm, which helps farmers control pests. Some farmers are now using agricultural robots to perform the most time-consuming and physically demanding farm tasks, which can help farmers save money on manual labor and reduce the workload of workers.
artificial intelligence
An interdisciplinary area of research called Artificial Intelligence aims to replicate human intelligence in robots similar to human cognition and behavior, including learning and problem-solving. Research scientists and extension specialists are now using AI technology to solve problems in agricultural productivity.
AI technology can help farmers increase yields by assisting them in choosing the right crop type, adopting improved soil and nutrient management practices, managing pests and diseases, estimating crop yields and predicting commodity prices. AI uses deep learning, robotics, IoT, image processing, artificial neural networks, wireless sensor networks, machine learning, and other cutting-edge methods to solve agricultural challenges.
These AI technologies can now help farmers monitor some of the items they get from their farms in real time, such as weather, temperature, water consumption or soil conditions, to better inform their decisions. AI is being used to develop smart agricultural practices that reduce losses for farmers while providing them with high yields.
From mobile facial recognition applications to self-driving cars, AI is everywhere today. Although productivity in other industries has also increased considerably thanks to AI systems and machine learning techniques, it is impossible to imagine agriculture undergoing a digital transformation.
Still, AI is bringing one of the oldest industries into the future. The number of applications of AI in agriculture is staggering, and AI makes precision agriculture possible. AI can help farmers irrigate, rotate, harvest, crop selection, planting, and pest control with data from machine learning technology.
Every industry is looking to use smart machinery to automate specific jobs. It happens when human intelligence is defined so that machines can understand it. In addition, AI technology in agriculture, which can perform tasks ranging from simple to complex, also has the potential to improve the world. The goal of machines is learning, reasoning, and perception, which contributes to the automation of work in various industries, and the use of intelligent machines simplifies various tasks.
limit
The lack of simple solutions to seamlessly integrate and embed AI into agriculture is a significant barrier to widespread adoption of AI in agriculture. Most farmers lack the time and digital skills to research AI solutions on their own, and in order to seamlessly integrate and embed AI into agriculture, these new AI solutions will need to be integrated into existing and legacy infrastructure and systems that farmers are already using.
Agriculture cannot rely entirely on AI because it cannot work outside of its programming. Farmers, especially in rural areas, lack technical knowledge and are unaware of the existence of such technologies. As awareness grows and technology becomes more accessible to the average farmer, agriculture is likely to become semi-autonomous, led by artificial intelligence systems that require vast amounts of data to train machines and make accurate predictions.
In situations where mainly farmland is predominantly farmland, spatial data is readily available, but obtaining temporal data is challenging. Most crop-specific data is only available once a year when the crop is grown, and it takes a lot of time to build a robust machine learning technology model because the data infrastructure takes a lot of time to mature.
Future scope
In the future, AI technology is providing innovative and precise solutions to major agricultural problems facing farmers around the world. AI will provide solutions to almost any problem, from pest control and weather forecasting to agricultural work assistance.
In the future, AI will help farmers become agricultural scientists, using data to optimize the yield of individual rows of plants. AI companies are developing robots that can easily perform a variety of tasks in a farm environment, designed to pick crops faster and more thoroughly than humans. These robots are taught to harvest and pack crops while checking crop quality and weeds. These robots can also overcome the difficulties faced by agricultural labor.
AI is helping farmers automate their agriculture, while also taking action to achieve precision planting to increase crop yield and quality while utilizing fewer resources.
Future technological developments will help businesses interested in improving AI-based products or services, such as training data for agriculture, drones, and automated machines, enabling the globe to address the food supply challenges facing a growing population.
Since AI maximizes resource utilization and efficiency, and solves resource and labor shortages to a large extent, it will be helpful and effective for the agricultural industry, and the technology will also play an important role in research and development in the field of horticulture.
AI technology combines data such as temperature, precipitation, wind speed and solar radiation with machine learning algorithms and images taken by satellites and drones to predict weather conditions, analyze crop sustainability, and assess the presence of pests and diseases on farms and plant nutritional deficiencies. Farmers with Wi-Fi connectivity may use AI apps to receive farm plans tailored for AI.
Using AI-driven solutions to increase yields and incomes without reducing priceless natural resources, farmers can meet global demand for higher food availability and profitability. Farmers can use AI to get real-time information from their fields to identify areas that need irrigation, fertilization or pesticide treatment.
Innovative agricultural practices, such as vertical farming, may help increase food production while using fewer food resources, resulting in less herbicide use, improved harvest quality, increased profits, and significant cost savings.
AI tools collect high-resolution aerial imagery and data about the irrigation systems needed for farmland, and AI helps detect soil problems such as blockages and leaks; It assesses and evaluates the harsh conditions of soils; The help of artificial intelligence is in improving agricultural productivity. AI-assisted picking, packing, and sorting improves food production, packaging, and sorting. Farmers benefit from it to make sense of agricultural data insights related to temperature, precipitation, wind speed, and solar radiation.
AI will be used in agriculture to improve the entire agricultural process.