Making Agriculture Smarter with Artificial Intelligence

According to BI Intelligence Research, AI and Machine Learning spend on AI-powered Agricultural Technologies is projected to triple in revenue by 2025, reaching $15.3 Billion.

PwC states that IoT-enabled Agricultural monitoring system, connected agriculture’s rapidly progressing technology is projected to reach $4.5 Billion by 2025.

Agriculture is one of the most important pillars that plays a significant role in economic growth. Agricultural automation is an emerging subject across the world. As demand for food and agricultural supplies increases, the technology to make things easy for farmers will parallelly rise. 

AI in agriculture helps in saving excess use of water, management of herbicides, pesticides, analysis of soil fertility, increasing efficiency in man-power used in agriculture and ultimately improving the crop quality. 

Drones are coming in handy to spray and monitor crops and with Computer Vision technology and accurate data annotation, they can revolutionize the entire process in the near future. 

How can Agriculture Benefit from Artificial Intelligence?

In agriculture, one has multiple, time-bound processes to keep a track of and excel at. Keeping a track of acres of farming area and regularly monitoring the health of the crops can be a daunting task. 

External factors like weather, seasonal sunlight, animal and bird migratory patterns, fertilizers, planting, and irrigation cycles impact the yield. All these data points prove to be a perfect problem for machine learning to solve. 

Data is in abundance and the success of a crop cycle seems like an ideal issue for artificial intelligence to solve and help farmers derive maximum yield and impeccable quality crops every square foot. 

Applications of Artificial Intelligence In Marketing

  1. Crop and Soil Monitoring and Surveillance
  2. Intelligent Robotics in Agriculture
  3. Price and Demand Forecasting of Crops

Crop & Soil Monitoring and Surveillance

Crop and soil health assessment is a crucial task and it has been the key to a successful farm and agricultural economy. The drone and satellite imaging technology provide 24*7 assistance in monitoring crops. 

Farmers often want to understand where the soil is degrading and what better than AI-powered technology to understand the same. Drone imaging gives highly valuable patterns in this area and is widely adopted by farmers across the world. 

This technology helps farmers understand how much pesticide or fertilizer to use on the crops, thus reducing costs and impact to the environment. 

Companies Using Computer Vision and Deep Learning Techniques to Identify and Diagnose Crops

PEAT – Machine Vision for Soil Defects

United States Department of Agriculture has estimated that the annual cost of soil erosion is $44 billion.

A berlin-based agriculture startup PEAT has an effective solution to reducing the impact of this problem. Using a deep learning application called Plantix they are successfully identifying defects and nutrient deficiencies in the soil. 

Their app identifies defects by analyzing the images uploaded by farmers using a smartphone camera. The app then provides easy and effective solutions to the farmers to restore the nutrition in the soil. Check out this video:

Data Annotation for Optimal Crop and Soil Defect Identification

As we know that machine learning models both computer vision and deep learning are most effective when they are fed with humongous high-quality data. 

Object detection training for models is crucial, the images captured using drones are collected and annotated, this creates a rich pool of training data that can be used to create highly efficient ML models. 

Without accurate and smart data annotation no model can give high accuracy on predictions. 

Manual Data Annotation is a Daunting Task – Know Why?

Intelligent Robotics in Agriculture

Weed Control

Every square kilometer of land is valuable for a farmer, the focus is to grow crops that sell. A common challenge faced by every farmer is the amplified growth of weeds. 

According to a study, 250 species of weeds have become resistant to herbicides. Weed Science Society of America conducted a study on the impact of uncontrolled weeds and estimated that farmers incur an annual loss of approximately $43 Billion each year because of weeds.

Blue River Technology for Weed Control

Protecting crops from weeds is an important task hence blue river technology has developed a robot called See and Spray. This robot is powered by computer vision technology and precisely uses weed sprays on plants. Take a look:

The company claims to eliminate 80% of chemicals normally sprayed on crops and reduce herbicide expenditures by 90%.

Harvest Robots

Harvest CROO Robotics has developed a robot to help strawberry farmers pick and pack their crops. Hiring laborers to perform this task is an expensive affair but getting a quality workforce is also a difficult task. 

Lack of labor has caused a lot of revenue loss to farmers in regions such as California and Arizona. Harvest CROO claims that their robot can harvest 8 acres in a single day and replace 30 human laborers. Check it out:

Data Sourcing Methods for Computer Vision Models to Train Robots

Automatic harvesting by robots can be classified into two important tasks:

  1. Detection of harvestable crops
  2. Robot arm motion to the position of the detected crop and harvesting the same without damaging the crop

To detect the pickable crops computer vision techniques are used. The data is captured by the color, spectral or thermal cameras are widely used by machines to understand the difference between harvestable and non-harvestable crops. 

When the robot uses a thermal camera the data annotation techniques identify the pickable crop based on the temperature difference between the crop and the background. While using a color camera the annotation happens based on red, green, and blue (RGB) color difference to segment the crops. 

Price and Demand Forecasting of Crops

Price forecasting intends to be useful for farmers, policymakers, and agribusiness industries. The recent innovation in Artificial Neural Network (ANN) modeling methodology has been demonstrated using monthly wholesale price series of various crops. 

The accuracy of crop price forecasting techniques is important because it enables the supply chain planners and government bodies to take appropriate actions by estimating market factors such as demand and supply. 

Factors and Data Points in Consideration by Machine Learning Models to Predict Accurate Price Forecasting:

  1. Historical price and quantity of crops that arrive in the market
  2. Historical weather data that influence crop production and transportation
  3. Data quality-related features gathered by past statistical analysis

Using these data points machine learning models can detect decent prices for their produce. Government can also impose an MEP (Minimum Export Price) so that exporters are forced to sell locally, thus bringing down the crop prices. It also helps farmers know the optimal value of their crops and identify the right time to sell to get maximum return on investment. 

Data Annotation in Agriculture

Image Annotation uses in Agriculture:

  1. Monitoring Health of Crops
  2. Geo-Sensing of Fields
  3. Live Stock Management
  4. Harvest Quality Optimization
  5. Soil Quality Analysis and Improvisation

Image annotation is a data labeling technique used to make varied objects recognizable for machines. But the data is in high volumes and hence manual annotation is not recommended. 

Manual Data Labeling Challenges:

  • Managing and maintaining the quality of data labeling
  • Workforce management 
  • Keeping a track of the cost incurred
  • Compliance with data privacy requirements
  • The task to ensure data security

Labellerr – The Fastest and Smartest Data Annotation Platform

Labellerr provides you with a simple, feature-rich, affordable data annotation solution.

Why Choose Labellerr?

  1. Data Labelling at scale is an important concern for an organization since creating labels on large data sets by hand is often too slow and expensive. Labellerr solves this problem with their agile ML-Powered data annotation platform. 
  2. Work Quality and Worker productivity is difficult to track in the case of crowdsourcing and freelance data labeling services. Hence now with Labellerr’s marketplace, you can choose from our hand-picked and most trusted vendors to get data labeling tasks done.
  3. Domain and context capabilities specific to tasks are limited with workers on crowdsourcing platforms, contractors, and freelancers. 

So, if you wish your data annotation task to be automated and error-free then choose Labellerr. 

http://www.labellerr.com

Benefits of Labellerr’s Data Annotation Platform:

  • Label data at 10x speed using Labellerr’s ‘Auto Labeling’ feature
  • Track work quality and worker productivity with a personalized dashboard experience.
  • Get relieved from the hassle of reviewing each dataset, instead review only the ones having low confidence scores. 

The above is still just an indicative list, the superior benefit of adopting an automated way of labeling dataset is peace-of-mind and trust. The idea here is simple – let the machine do all the work for you so that your focus can be on your customers!

If you wish to try the power-packed machine learning embedded data annotation platform – Labellerr then just click here and explore.

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