Artificial Intelligence in Agriculture Sector

The Artificial Intelligence in agriculture sector is one of the major industries which are revolutionized by the Artificial Intelligence technology.
artificial intelligence in aggriculture


The agricultural industry is largely dependent on uncontrollable factors like climate change, population growth, and other environmental conditions.

Artificial Intelligence in agriculture sector can be implemented for various technological advancements. These include Machine Learning services, Artificial Intelligence consulting services, data analytics, internet of things and availability of sensors and cameras, etc. By analyzing the various data sources such as temperature, soil, weather, and historic crop performance, Artificial Intelligence in agriculture sector will be able to provide better predictive insights.

Machine vision technologies have the potential to revolutionize applications in the agricultural sector. The use of an AI in farming can be used in agricultural processes like harvesting, use of precise weed-killing chemicals, etc. Many agriculture websites as well as agriculture startups are opting for AI solutions and AI consulting to develop a path of steady progress.

Some Transforming Use Cases of Artificial Intelligence in Agricultural Sector are:

automativeWeather Prediction:

AI in Farming along with the satellite data can be used to predict the weather conditions analyze the crop sustainability and evaluate the farms for the presences of pests and diseases. The AI in farming is able to provide billions of data points including temperature, precipitation, wind speed, and solar radiation.

automativeSoil Health monitoring

Artificial Intelligence in agriculture applications can be used for identifying potential defects and nutrient deficiency in the soil. AI in farming can detect the deficiency in the soil. The image recognition and annotation services can identify the defects through the images captured by the user’s smartphone. The user is provided with various soil restoration techniques.

automativeMonitoring crop health

Artificial Intelligence in agriculture sector can be used for monitoring crop health and sustainability. For example detecting the diseases, pests, and nutrition of the farms. Artificial intelligence algorithm can tell where the fertilizer is needed on the farm. This can reduce the amount of fertilizer needed by 40%. The software can be used across various mobile devices.

The Machine Learning algorithm is most effective when they are fed with a large amount of high-quality training data.

Artificial Intelligence Agriculture Robots – AI Companies are developing and programming autonomous Artificial Intelligence in agriculture sector to handle essential agricultural tasks such as harvesting crops at a higher volume and faster pace than human laborers.

AI Agricultural Predictive Analytics – The Artificial Intelligence and Machine learning algorithm models are being developed to track and predict various environmental impacts on crop yield such as weather changes.


The Scope of AI in Agriculture

Agriculture is seeing rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) algorithms both in terms of agricultural products and in-field farming techniques. Cognitive computing, in particular, is all set to become the most disruptive Artificial Intelligence in agriculture sectors as it can understand, learn, and respond to different situations to increase efficiency.

Providing some of these solutions as a service like Chatbot or other conversational platforms to all the farmers will help them keep pace with technological advancements as well as apply the same in their daily farming to reap the benefits of this service.

Given below are the top Six areas where the use of cognitive solutions can benefit agriculture.

Huge volumes of data get generated every day in both structured and unstructured format. These relate to data on the historical weather pattern, soil reports, ML algorithm services new research, rainfall, pest infestation, images from Drones and cameras and so on. Proximity Sensing and Remote Sensing are two technologies and it is primarily used for intelligent data fusion.

Growth was driven by IOT:

One use case of this high-resolution data is Soil Testing. While remote sensing requires sensors to be built into airborne or satellite systems, proximity sensing requires sensors in contact with soil or at a very close range. This helps in soil characterization based on the soil below the surface in a particular place.

Image-Based Insight Generation:

In Image-Based insight generation, precision farming is one of the most discussed areas of farming today. Drone-based images can help in in-depth field analysis, crop monitoring, scanning of fields and so on. AI in farming, Business intelligence, and Human and Computer vision technology, IOT and drone data can be combined to ensure rapid actions by farmers. Feeds from drone image data can generate alerts in real time to accelerate precision farming.

Disease detection:

Pre-processing of image ensure the leaf images are segmented into areas like the background, non-diseased part, and diseased part. With the help of ai in farming, the diseased part is cropped and sends to remote labs for further diagnosis. It also helps in pest identification, nutrient deficiency recognition and more.

Crop readiness identification:

Images of different crops under white/UV-A light are captured to determine how ripe the green fruits are. AI in Farming can create different levels of readiness based on the crop/fruit category and add them into separate stacks before sending them to the market.

Health Monitoring Crops:

Remote sensing techniques along with hyperspectral imaging and 3d laser scanning are essential to building crop metrics across thousands of acres. It has the potential to bring in a revolutionary change in terms of how farmlands are monitored by AI in farming both from time and effort perspective. This health monitoring crops technology will also be used to monitor crops along their entire lifecycle including report generation in case of anomalies.

Automation Techniques in irrigation and enabling farmers:

In terms of human-intensive processes in farming, irrigation is one such process. Artificial Intelligence in agriculture sector and Machines trained on the historical weather pattern, soil quality, and kind of crops to be grown, can automate irrigation and increase overall yield. With close to 70% of the world’s fresh water being used in irrigation, automation can help farmers better manage their water.

How we can help?

With better results of Artificial Intelligence in Agriculture Sector, you need to make sure that your algorithms are optimized.For accurate results, you need to have a large amount of data which should be high quality and human-in-the-loop annotated.We provide you with the best-curated ai in farming services, data samples and help you to gather a large amount of data in a limited time. Contact Webtunix AI if you want assistance in Artificial Intelligence in Agriculture Sector.

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