Use Case Of ML/AI In Agriculture

Artificial Intelligence(AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and the programs make the system AI-enabled. The applications for artificial intelligence are endless. The technology can be applied to many different sectors and industries.

Here, I'll talk about how AI is helping farmers to improve their efficiency and reduce environmental hostile impacts. Because everybody talks about the big MNCs like how Google, Facebook, Amazon, etc are using AI/ML but very few are here who talk about Agriculture. The Agriculture sector is the foundation of the world’s economy and with the increasing population, the world will need to produce 50% more food by 2050.

Now AI is expanding its footprints at the ground level making a significant impact in the world’s most vital sector — Agriculture. After the healthcare, automotive, manufacturing, and finance sector now artificial intelligence in agriculture is providing cutting-edge technology for harvesting with better productivity and crop yield.

Implementing AI-empowered approaches to help farmers get more from the land i.e. detect diseases or climate changes sooner and respond smartly.

The Market study report stated that the global Artificial Intelligence (AI) in Agriculture market size is expected to reach 1550 million US$ by the end of 2025.

Artificial Intelligence in the Agricultural And Farming

Precision Farming with Predictive Analytics

AI applications in agriculture expanded into doing accurate and controlled farming by providing proper guidance to farmers about Optimum Planting, Water Management, Crop Rotation, Weather Forecasted, Timely Harvesting, Nutrient Management, and Pest Attacks.

aWhare, a Colorado-based company uses machine learning algorithms in connection with satellites to predict the weather, analyze crop sustainability, and evaluate farms for the presence of diseases and pests.

FarmShots-Based in Raleigh, North Carolina, FarmShots is another startup focused on analyzing agricultural data derived from images captured by satellites and drones. Specifically, the company aims to “detect diseases, pests, and poor plant nutrition on farms.”

Soil and Crops Health Monitoring

Now there is a big challenge comes up due to continuous deforestation and degradation of soil quality for food-producing countries. But now Berlin-based agricultural tech startup PEAT has developed a deep learning application called Plantix that reportedly identifies potential defects and nutrient deficiencies in the soil. An analysis is conducted by software algorithms that correlate particular foliage patterns with certain soil defects, plant pests, and diseases.

The app identifies possible defects through images captured by the user’s smartphone camera. This app works on image recognition based technology. By this user will get the information about the soil restoration techniques, tips & also detect the defects in the plants and other possible solutions as explained in the short video below:

SkySquirrel Technologies Inc. - Drones and Computer Vision for Crop Analysis

SkySquirrel Technologies Inc. is one of the companies bringing drone technology to vineyards. With this technology, the company's aim to help the user to find solutions for monitoring crop's health using drones. The user pre-programmed the route of the drone so that it can collect the data from the vineyards field. After completion of the route by the drone, data is transferred via a USB drive from the drone to a computer for analyzing purposes.

The company provides a detailed report containing the current health of the vineyard to the farmers behind this company uses the algorithms to analyze the captured images, specifically the condition of grapevine leaves. Since grapevine leaves are often telltales for grapevine diseases such as molds and bacteria.

Trace Genomics – Machine Learning for Diagnosing Soil Defects

Similar to the Plantix app, California-based Trace Genomics, provides soil analysis services to farmers. These apps help farmers to monitor the soil and crop’s health conditions and produce a healthy crop with a higher level of productivity.

According to the company’s website, after submitting a sample of their soil to Trace Genomics, farmers will receive an in-depth summary of their soil contents that is analyzed by the experts.

Agricultural Robotics

Some AI companies are developing robots or bots to help farmers to perform multiple tasks in the farming field easily. AI bots in the agriculture field can harvest crops at a higher volume at a much faster pace compared to human labor. By leveraging computer vision helps to monitor the weed and spray them. Thus, Artificial Intelligence is also helping farmers find more efficient ways to protect their crops from weeds.

Blue River Technology – Weed Control

According to its website, the company claims that its precision technology eliminates the volume of chemicals normally sprayed on crops to 80% and also can reduce herbicide cost by 90%. 

Harvest CROO Robotics – Crop Harvesting

Automation is also emerging in an effort to help address challenges in the labor force. A company, named Harvest CROO Robotics has developed a robot that helps farmers pick and pack their crops like strawberry. The Company claims that its robot can harvest 8 acres in a single day and replace 30 human laborers.

Models Behind

Though it is always fascinating to read about the future, the most important part is the technology that paves the way for it. Agricultural machine learning, for instance, is not a mysterious trick or magic, but a set of well-defined models that collect specific data and apply specific algorithms to achieve expected results.

So far, the distribution of machine learning is unequal throughout agriculture. Mostly, machine learning techniques are used in crop management processes, following with farming conditions management and livestock management.

The literature review shows that the most popular models in agriculture are Artificial and Deep Neural Networks (ANNs and DL) and Support Vector Machines (SVMs).

ANNs are inspired by the human brain functionality and represent a simplified model of the structure of the biological neural network emulating complex functions such as pattern generation, cognition, learning, and decision making. Such models are typically used for regression and classification tasks which prove their usefulness in crop management and detection of weeds, diseases, or specific characteristics. The recent development of ANNs into deep learning has expanded the scope of ANN application in all domains, including agriculture.

SVMs are binary classifiers that construct a linear separating hyperplane to classify data instances. SVMs are used for classification, regression, and clustering. In farming, they are used to predict the yield and quality of crops as well as livestock production.

More intricate tasks, such as animal welfare measurement, require different approaches, such as multiple classifier systems in ensemble learning or Bayesian models — probabilistic graphical models in which the analysis is undertaken within the context of Bayesian inference.

Though still at the beginning of its journey, ML-driven farms are already evolving into artificial intelligence systems. At present, machine learning solutions tackle individual problems, but with further integration of automated data recording, data analysis, machine learning, and decision-making into an interconnected system, farming practices would change into with the so-called knowledge-based agriculture that would be able to increase production levels and products quality.

So, here, I discuss some use cases that how AI is can be used in Agriculture and its application in Farming.