In this new and modern digital age, artificial intelligence is being incorporated into vital human activities creating more efficiency and enhanced productivity. Not to be left behind, the agricultural sector has made great strides through AI-powered computer vision models in crop monitoring and production.
The role of AI robots, drones, and other automated machines is becoming ever more vital in planting, health monitoring, harvesting, and enhancing crop productivity. But have you ever sat back and asked yourself how these AI-powered machines aid in meticulous agriculture and farming?
Surprisingly, AI-powered computer models are made possible through computer vision technology. To achieve this, the AI models are thoroughly trained using annotated/labeled images fed to them using the correct machine learning algorithms.
Image Labeling for Machine Learning in Agriculture
Image labeling services in the field of agriculture aids in performing different actions e.g. identifying crops, fruits, vegetables, or weeds. Once enough annotated data is uploaded onto the deep learning algorithm, an AI-powered computer model becomes intelligent enough to predict and perform human functions like sorting fruits and monitoring crop health.
Through the data labeling process, image and data annotation is playing an ever-increasing role in applying artificial intelligence and machine learning to agricultural data. Below are some of the ways in which image annotation is applied to machine learning for use in agriculture.
Precise Agriculture using Robots
Robots are steadily emerging as the number one preference for farmers on farm fields. In agriculture, AI-powered robots are performing different actions with aid from machine vision algorithms. These robots can perform actions such as; plowing, planting, weed detection, and monitoring crop productivity and overall health. They also aid in picking fruits and vegetables, sorting, and packaging them accordingly. In addition to this, robots use computer vision cameras to the group and classify different farm produce faster and with improved accuracy.
With the aid of deep learning algorithms, it is easy for an AI-powered robot to identify faults from multiple angles using color and geometrical variations. The deep learning algorithms work by first identifying and locating the fruits and then moving to classify them appropriately.
In order to equip AI-powered algorithms, accurately annotated images of plants, crops, and floras are fed into the models. Through bounding box annotation services, AI-powered robots are trained to recognize and detect different crops, weeds, fruits, and vegetables.
Sorting Fruits and Vegetables
Once all the fruits and vegetables have been collected, the task of sorting is done by AI-powered robots to separate the healthy from the rotten fruits and vegetables. Through the use of training data, accurately labeled images based on deep learning are used to sort and grade farm produce.
Likewise, AI robots are able to sort flowers, stems, and buds of various breeds, shapes, and sizes. These models are trained to be at par with the strict international rules and standards of the different crops, fruits, and flower markets.
Monitoring Soil, Crop, and Animal Health
Through the application of Geosensing technology, drones and other autonomous flying objects are able to determine the health condition of both crops and soil. This aids in informing farmers on what is the right time for sowing and what actions ought to be taken to save vulnerable crops. To maximize crop yields, the correct soil conditions and timely insecticides are key. In addition, AI-powered technology makes it easier to highlight the health of both crops and animals.
The health of animals is typically given by veterinarians. This is due to the fact that animal health often dictates the animal’s reproductive health, milk production, and feed intake making livestock rearing ever more profitable.
Crop Yield Forecasting Using Deep Learning
By applying AI in agriculture using deep learning data sets, predicting the expected crop yield using smart devices to analyze the data.
Developing deep learning platforms needs in-depth knowledge to facilitate the training of accurate and reliable predictions. To train these algorithms, ample amounts of accurately annotated data are needed to develop such computer models.
AI in Forest Management
Artificial Intelligence is used in forest management by utilizing areal images taken by planes, satellites, and drones. Images captured by the said sources provide the raw training data to be later annotated/labeled.
When Machine learning models are trained using accurately annotated data, the models are able to better detect illegal activities like tree cutting which eventually leads to deforestation damaging the Ecosystem. Assessing the growth and overall health of trees using AI models equips forest management stakeholders to make informed and better decisions when monitoring forests.
Image Labeling in Deep Learning for Agriculture
Getting a hand on top quality training data for machine learning is no mean feat for Machine Learning companies who work to develop AI models. But this Goliath of a challenge is made simpler with the help of data annotation companies like Impact Outsourcing.
We assist AI companies to annotate and labeling training data for computer vision at a fraction of your initial cost while maintaining the same high-quality levels as expected.
Impact Outsourcing is famous for providing training datasets for machine learning in numerous fields. These include; Agriculture, Healthcare, Retail, Autonomous Vehicles, Autonomous Flying, and Satelite Imagery.