Agriculture Transformation: Sowing AI for the best yield!

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The power of AI to help improve crop production and increase food security.

The agriculture industry has undergone changes in many ways due to technological advances. Agriculture has been and will always be the mainstay occupation in many countries. Agriculture is a $5 trillion industry!! By 2050 it is estimated that the human population will reach around 9.9 million. This will lead to more pressure on land to yield more food to feed the teeming millions! And the burden increases with climatic changes affecting the already scarce resources of water and farmable land – only an extra 4% will come under cultivation by 2050!


Through the decades there have been many saviors in terms of technology uses be it mechanized or computerized. These have helped humans survive and thrive.

The human observational tasks are now being carried out by computer vision technology right from crop and soil monitoring to detecting disease and providing for predictive analysis! Artificial Intelligence is no longer used for just eCommerce and healthcare and finance domains but has slowly proven its mettle in the field of agriculture too!

A few stats that show how significant AI is in the agriculture industry:

  • A Forbes report states that integrating AI and Machine Learning into the agriculture industry is projected to triple to 15.3 billion by 2025.
  • Another research states that “The global AI in Agriculture Market is expected to grow at a compound annual growth rate (CAGR) of 20% from 2019 to 2026”

Let’s dig deeper into the ground to see what is happening beneath, shall we?

Crop and soil monitoring

Two essential factors that determine crop health and the quality and quantity of yield are the micro and macro nutrients present in the soil.

It’s essential to have healthy micro and macronutrients for improved soil health.
For a healthy harvest, constant watch on the growing crops and how the macro and micro environment affect the crop is critical.

Both soil and crop health have so far depended on human observation and intervention. The art is slowly dying due to the dependence on technologies that provide greater accuracy and timeliness in predictions.

Drones(UAVs) capture aerial images whose data is now analyzed by computer vision models trained to

  • Check crop and soil health
  • Predict yield predictions with greater accuracy than human observations.
  • Alert crop maladies even before they are noticed by humans

By ensuring the above steps, AI models can thus alert farmers to take the necessary steps to avoid crop destruction.

How does AI provide for healthy crops?

Keeping a Watch on crops

AI is known to take over labor-intensive manual repetitive tasks so that people can focus on other critical tasks. The same is applicable in the agriculture industry.

Studies have been carried out on various crops to understand their growth and this data has been fed to the computer vision models. And voila, computer vision models have helped predict at what stage in the growth of the crop or its ripeness they are ready to be harvested. This leaves the farmer more time to do other tasks than walk across the field trying to ascertain when to start harvesting.

Analyzing the Soil

Computer vision can analyze all aspects of soil while using a simple microscope and help farmers lower laboratory processing and costs

Crops require 3 main nutrients in the soil namely, nitrogen(N), phosphorous(P), and potassium(K).  The deficiency of nutrients can lead to poor quality of crops.

AI-based applications can help identify nutrient deficiencies in soil including plant pests and diseases. This supports farmers to get an idea of which fertilizer or nutrient should be added to improve harvest quality.

Detection of plant disease and insect attack

With the help of AI models using image classification, detection and image segmentation based on deep learning techniques on trained data sets, plant diseases and pest attacks can be easily detected.

In one case study, researchers used a Deep Convolutional Neural network to annotate the stages of rot severity on apples. The AI model identified and intelligently diagnosed the stage of severity with a 90.4% accuracy! This reduces the human-intensive intervention saving time and effort.

In another study, they used an improved YOLO v3 algorithm trained on a dataset of photos of pest-affected tomatoes. When the diagnostics were run, the accuracy was 92.39% detection of pests on tomato plants in just 20.39 ms!

Counting the bugs

With computer vision systems in place, not only can farmers know if the crop is infested with pests but also the number of pests and the type of pests that have affected the crop can be determined in a matter of hours instead of days! 

And not just for those on the crops that crawl around the soil but also for the flying insects! Using YOLO object detection, classification, and Support Vector Machines (SVM) a lot can be achieved!

Monitoring Cattle and farm animals

Besides agriculture in terms of crops and plants and soil, computer vision using advanced AI models can monitor, track, and detect farm animals and their movements.

Unusual behavior, detecting disease, counting animals, and checking on missing ones, can all be done with data collected from drones and cameras across the landscape- not just during good weather but more when sudden bad weather strikes and it makes it impossible for farmers to step out.

Any deviation from normal behavior patterns can be immediately alerted using the AI models trained on data sets.

Precision spraying

With the help of UAVs guided by computer vision AI, precision spraying of crops can be achieved. This can be done in a uniform manner, keeping to the areas and amount required. This can notably decrease contamination of healthy crops and refrain from affecting other agricultural elements like livestock and water being affected by the spraying. 

Challenges do exist when it comes to spraying crops by human intervention but decrease when AI methods are introduced. There have been experiments that devised smarter and better ways of spraying using computer vision-aided controlled devices. The accuracy is of utmost significance, and this can be achieved when AI is integrated into the devices for better control.

Besides spraying, robots can also be engaged in the long term. This would help farmers reduce days of wasted output on physical labor as against mechanized and AI-controlled robotic devices.

With such AI-driven robotic devices, trained on a dataset of identified weeds based on leaf, color, size, depth, etc., the precision of weed dismissals can be done in a matter of hours rather than days. This can bring much respite to farmers and save them from much back-bending work leading to better crop yields and quality produce!

Drone Surveying and Aerial Imagery

Besides agriculture monitoring in terms of soil and crop, and livestock, drone surveying has been employed to survey the surrounding landscape to evaluate minor conditions of rockfalls, forest fires that could affect crops due to winds, and thereby saving crops and livestock from harm.

Aerial imaging can support and assist farmers when AI models get trained on more and more data sets. This as a result helps evolve patterns to help detect and monitor with precision and increase crop yield at low costs.

Earlier livestock was used to save the farmers from dreary work, now it’s time for the AI-aided devices like drones to become the draught animals!

Post harvesting Ai driven produce

Computer vision can help farmers through the entire driven agricultural cycle from monitoring the health of soil and growth of the crop, weeding out unwanted weeds that could lower crop yield, accurately spraying herbicide on crops with targeted precision, and monitoring livestock from going astray to even after the crop is harvested. 

How can Computer Vision AI-driven models help in post-harvested produce?

They can help during the winnowing and help with removing the chaff from the food grain and fruits and vegetables can help determine good from not-so-marketable produce. If AI models are trained to detect the “good” produce in terms of size, color, weight, and defect free, from the “bad” produce i.e., discolored squishy produce, this can go a long way in creating a market for healthy fruits and vegetables and thus save the farmer from the labor-intensive manual work.

A recent study that used AI with machine learning was able to even grade vegetable quality with 95.5% accuracy by using image data with certain pre-defined input features used to grade the vegetable.

Farmers adopting AI-driven technologies?

The technology used to improve the way farming has been done is nothing new to farmers. They are constantly innovating ways to make their tasks less manual intensive and improve efficiency plus increased productivity with good health yield. This has been since incipient farming began.

Computer vision with AI-driven devices can take this step forward and help solve the challenges that lie ahead, especially feeding the population growth given the stressed cultivable land and resources that we currently have at our disposal.

Adopting new technologies, especially drones, and robotics devices may tend to be too much to handle for any farmer. Add monitoring data and predictive analysis and there would be an initial resistance. But when shown how AI-driven devices can leverage the agricultural industry long term and protect the environmental resources, decrease labor-intensive tasks, with focus on the market, what farmer wouldn’t jump at the chance for a multi-faceted assistant who won’t grumble but do the work diligently and without complaints while bringing home the dollars?

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