Artificial intelligence has become one of the most important technological developments across many sectors, including banking, robotics, agriculture, and education. It is fundamentally changing the agricultural industry and has a significant impact on it.
The global population is growing, and there is a daily increase in the demand for food. Agriculture is vital to any country’s economic sector. The farmers’ traditional methods are currently unable to match the demand. Numerous creative ways to automate have been developed in order to meet these objectives and provide many workers in this area with excellent job chances.
Artificial Intelligence in Agriculture
Farmers profit from it in a number of ways, including by growing healthier crops, managing pests, keeping an eye on the soil, and other ways. Today’s cutting-edge method in agriculture is artificial intelligence. The following are a few important applications of artificial intelligence in agriculture:
1. Crop health monitoring
The kind of soil and nutrition of the soil has a significant impact on crop quality. However, it is difficult to assess soil quality because of the daily decline brought on by the rate of deforestation. AI has developed a brand-new program named Plantix to address this problem. This application can give farmers ideas on how to apply better fertilizer to increase the quality of their harvest.
PEAT was created in order to detect soil inadequacies, such as plant pests and illnesses. Farmers can use this app to take pictures of their plants and learn information about their quality thanks to AI’s image recognition capability.
2. Forecasting the weather and price
Farmers find it difficult to choose the best options for harvesting, sowing seeds, and sole preparation due to climate change, as we detailed in the difficulties.
With the use of price forecasting, farmers may have a better idea of the price of crops for the future few weeks, allowing them to maximize profit. However, farmers can learn about weather analysis and prepare for the type of crop to produce, the seeds to sow, and crop harvesting by using artificial intelligence (AI) weather forecasting.
3. Robotic agriculture
Currently, numerous AI companies are creating robots for use in the agriculture industry. Robotics is widely employed in many industries, mostly in manufacturing, to carry out difficult jobs.
AI robots are also taught to inspect crops for quality, spot and eradicate weeds, and harvest crops more quickly than a human. These AI robots are designed so that they can handle a variety of farming activities.
4. Diagnoses of illnesses
First, plant images are pre-processed using computer vision technologies. This ensures that plant images are correctly divided into portions that are healthy and sick. Farmers may easily learn about diseases using AI forecasts. They can swiftly and accurately detect ailments with this. It can save farmers’ time as well as the life of plants.
After being discovered, the diseased region is excised and sent to the lab for further analysis. Among many other things, this technique helps in the detection of pests and nutrient deficits.
5. Adaptive Spraying
Cannabis can be quickly and easily recognized with AI sensors, which can also identify weed-affected locations. A variety of AI firms are developing robots with computer vision and AI that can precisely spray weeds.
The usage of AI sprayers can significantly reduce the number of pesticides needed to be applied to fields, improving crop quality and reducing costs. Herbicides may be precisely applied in these locations after locating them, which reduces the need for herbicides while also saving time and crop.
6. Precision farming
Precision farming technology, which is significantly more exact and controlled, can take the role of the labor-intensive part of farming, which requires completing repetitive tasks.
The three pillars of precision farming are “Right place, Right timing, and Right goods.” One example of precision farming is the measuring of plant stress levels. This information can be obtained using high-resolution images and data from numerous plant sensors. The sensor data is subsequently loaded into the machine-learning model for stress recognition.
AI’s Advantages In Agriculture
AI improves decision-making
The agriculture sector greatly benefits from predictive analytics. AI-powered equipment can assess the health of the soil and the crops, suggest fertilizer applications, track the weather, and assess crop quality. It aids farmers in overcoming the major difficulties they face in farming, including analyzing market demands, predicting prices, and determining the best window of time to plant and harvest a crop.
The farmers can make better decisions and practice effective farming thanks to all these benefits of AI in agriculture.
AI reduces expenses
Farmers can make informed decisions at every stage of farming thanks to the real-time insights that AI offers them. With fewer labor and resources required, farmers may grow more crops by using precision farming techniques and AI-enabled machinery.
With this wise choice, there will be less product and chemical loss and more effective use of both time and money. Together, these factors lead to a decrease in the usage of pesticides, improved crop quality, and more profit while using fewer resources.
AI decreases the labor shortage
AI has the potential to tackle the automation challenge in agriculture. A lack of labor has long been a problem for the agricultural industry. Machines and equipment powered by AI are noticeably faster and more precise than farm workers.
Thanks to AI and automation, farmers may fulfill jobs without adding more employees. Driverless tractors, intelligent irrigation and fertilization systems, smart spraying, software for vertical farming, and harvesting robots powered by AI are a few examples.
Conclusion:
The premise behind artificial intelligence is that human intelligence can be characterized in a way that makes it easy for a machine to replicate it and complete tasks of any complexity. The three main goals of artificial intelligence are learning reasoning and perception.
Even though some extensive studies are ongoing and some apps are currently available on the market, the agricultural industry still needs more support. The adoption of AI solutions will have a significant impact on the future of AI in farming. Additionally, the development of predictive solutions to address a genuine problem encountered by farmers in farming is still in its early stages.
