How Could Machine Learning Or Broadly AI Help Agriculture?
Can you imagine an industry that involves more risks than agriculture? You reap what you sow, they say. When the weather hits or crops are affected by the disease, farmers can barely talk about yields. Or when a global pandemic hits, it suddenly becomes more difficult to manage various processes because most of them are not digital.
At the same time, the world population is growing and urbanisation continues. Disposable income is increasing and consumption habits are changing. Farmers are under a lot of pressure to meet growing demand and need a way to increase productivity. Thirty years from now, there will be more people to feed. And since the amount of fertile soil is limited, it will also be necessary to go beyond traditional agriculture.
We need to look at ways to help farmers minimise their risks, or at least make them more manageable. The implementation of artificial intelligence in agriculture on a global scale is one of the most promising opportunities.
AI can potentially change the way we view agriculture, allowing farmers to achieve more with less effort and providing many other benefits. As the next step on the path from traditional to innovative agriculture, Artificial intelligence services in Frisco can complement technologies already in place.
Agribusinesses need to know that AI is not a panacea. However, it can bring tangible benefits to the little things of everyday life and simplify farmers’ lives in many ways. So how can we use artificial intelligence for sustainable agriculture? What are the opportunities for AI in agriculture and how can AI help us address existing challenges?
How AI can be useful in agriculture:
Agriculture involves a series of processes and stages, most of which are manual. By complementing adopted technologies, AI can make the most complex and routine tasks easier. It can collect and process big data on a digital platform, propose the best course of action, and even initiate that action when combined with other technology.
Field condition management:
Soil management:
For the Best artificial intelligence company in Frisco involved in agriculture, the soil is a heterogeneous natural resource, with complex processes and vague mechanisms. Its temperature alone can give insight into the effects of climate change on regional performance. Machine learning algorithms study evaporation processes, soil moisture, and temperature to understand the dynamics of ecosystems and their impact on agriculture.
Water Administration:
Water management in agriculture affects the hydrological, climatological and agronomic balance. So far, the most developed ML-based applications are related to the estimation of daily, weekly or monthly evapotranspiration, which allows more effective use of irrigation systems and the prediction of daily dew point temperature, which helps identify expected weather events and estimate evapotranspiration and evaporation.
Crop management:
performance prediction:
Yield prediction is one of the most important and popular topics in precision agriculture, as it defines yield mapping and estimation, matching crop supply with demand, and crop management. State-of-the-art approaches have gone far beyond simple forecasting based on historical data, but incorporate computer vision technologies to provide on-the-fly data and comprehensive multidimensional analysis of crop, weather, and economic conditions to make the most of the performance of peasants and population.
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Disease detection:
Both outdoors and in greenhouse conditions, the most widely used practice in pest and disease control is the uniform spraying of pesticides over the growing area. To be effective, this approach requires significant amounts of pesticides, resulting in significant financial and environmental costs. ML is used as part of general precision agriculture management, where agrochemical input is targeted in terms of time, place and plants affected.
Drone Crop Health Analysis:
A Deep learning development company in USA has brought drone-based Aerial imaging solutions to monitor crop health. In this technique, the drone captures data from the fields and then the data is transferred via a USB drive from the drone to a computer and analysed by experts.
This company uses methods to analyse the captured images and provide a detailed report containing the current health of the farm. It helps the farmer to identify pests and bacteria, which helps farmers to timely use pest control and other methods to take necessary measures.
Precision agriculture and predictive analytics:
AI applications in agriculture have developed applications and tools that help farmers to carry out controlled and imprecise agriculture by giving them proper guidance on water management, crop rotation, timely harvest, the type of crop to be grown, optimal planting, pest detection. seizures, nutrition management.
Using the Best machine learning company in USA and AI technologies in connection with images captured by satellites and drones, AI-enabled technologies predict weather conditions, analyse crop sustainability, and test farms for the presence of disease or pests and poor nutrition of crops. plants on farms with data such as temperature, precipitation, wind speed and solar radiation.
Farmers without connectivity can benefit from AI right now, with tools as simple as an SMS-enabled phone and the Siembra app. Meanwhile, farmers with access to Wi-Fi can use AI apps to get a continuously personalised AI plan for their land. With such IoT and AI-powered solutions, farmers can meet global needs to increase food production and income sustainably without depleting valuable natural resources.
DIGITAL AGRICULTURE:
Crop production plays a critical role in the food and biofuel industries around the world, and ML is radically improving how ranchers contribute on both fronts.
Farmers make hundreds of complex and interconnected decisions each year that surface their risk, sustainability, and business return. By using sensors in the field coupled with ML-enabled digital applications, farmers now have the means to predict crop yields and assess crop quality, identify plant species, and detect crop diseases and pest infestations. weeds in ways that were previously impossible.
YIELD PREDICTION AND QUALITY ASSESSMENT:
Through the application of a Data science company in USA, a farmer can log into a custom dashboard on a computer or tablet and access an accurate assessment of harvestable vs. unharvestable acres on any given day. The weight and maturity of harvestable crops can also be measured and predicted.
In addition, using a variety of technologies, including image analysis, crops can be evaluated before and after harvest for the presence of desirable characteristics, the extent of damage (if applicable), nutritional composition, and other factors that may affect growth. the viable final performance and price of the product.
The future of AI in agriculture: farmers as AI engineers?
Throughout human history, technology has long been used in agriculture to improve efficiency and reduce the amount of intensive human labour involved in farming. From improved ploughs to irrigation, tractors, and modern AI, it’s an evolution that humans and agriculture have undergone since the invention of farming.
The increasing availability and affordable availability of computer vision will become another important step forward here.
AI and ML development has the ability to transform 21st-century agriculture by:
- Increase the efficiency of time, labour, and resources.
- Improve environmental sustainability.
- Make resource allocation “smarter”.
Provide real-time monitoring to promote increased product health and quality.
Of course, this will require some changes in the agricultural industry. Farmers’ knowledge of their “field” will need to translate into AI training, and this will depend on increased technical and educational investments within the agricultural sector.
But again, innovation and adaptation are nothing new in agriculture. Computer vision and agricultural robotics are just the latest way that farmers can adopt new technologies to meet the world’s growing demand for food and increase food security.
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About the Author
I am a passionate content writer and blogger who has written a number of blogs for mobile app development. Being in the blogging world for the past 3 years, I am currently contributing tech-laden articles and blogs regularly to USM Systems. I have a competent knowledge of the latest market trends in mobile and web applications and express myself as a huge fan of technology.
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I am working as a Marketing Associate and Technical Associate at USM Business Systems. I am working in the Internet of Things and Cloud Computing domain. I completed B.E. in Computer Science from MIT, Pune. In my spare time, I am interested in Travelling, Reading and learning about new technologies.
