How AgTech Is Growing Up With Machine Learning Techniques


The AgTech industry seems to have grown substantially over the past few years, and seems poised to continue its substantial growth rate for the foreseeable future. In 2018, AgTech as an industry reached almost $17 billion in funding stretching across 1450 different investment projects. As this industry continues to grow and mature, many companies and specialists from different fields are looking at the possibilities and wondering how their particular specialty can apply to the AgTech's sector. Deep learning and image recognition are two technologies that hold substantial potential for the AgTech industry.

What Is AgTech?

Let's take a moment to define AgTech itself, as it is one of those terms that is used by different people to mean different things. AgTech is an umbrella term encompassing many different technologies that center around the enhancement of agricultural operations. As agricultural operations are called on to enhance efficiency and produce more crop while reducing space and water usage, digital technologies can help farmers meet the increasing demand. Agricultural operations employ satellite data and aerial mapping, robotics, automated systems, and drones. These are only a few of the ways AgTech manifests itself.

The collection and analysis of data is key to enhancing efficiency. Monitoring devices like spray monitors and yield monitors produce vast amounts of data, but unless this data can be analyzed and meaningful trends pulled out of it, they aren’t very helpful. Data analysis is being used to predict potential harvests, optimize fertilizer distribution, and reduce pesticide drift. Many of these applications of AgTech rely on deep learning systems.

The Role Of Machine Learning/Deep Learning In AgTech

Machine learning architectures excel at analyzing features like:

  • Usage of nutrients and water

  • Changes in climate

  • Disease resistance

  • Pest control measures

  • Correlating different feature values with improvements in yield

Thanks to the implementation of machine learning architectures, plant cultivation has become substantially more efficient. Machine learning systems are provided with data about weather patterns, soil types, fertilizer use, etc. and then they create probability models based off of these variables.

Most of the major machine learning applications in the AgTech industry are related to the management of crops. Tasks like estimating yield from season to season for different crops based on variables like weather patterns and economic conditions, or classifying harvests by crop quality in order to reduce waste and scale prices, are common applications of machine learning techniques. Beyond crop management, machine learning techniques are frequently applied to analyses of farming conditions and livestock management. Estimating production of eggs and dairy products is commonly done through machine learning models.

The most commonly used algorithms for tasks like the classification and clustering of crops and livestock products tend to be Support Vector Machines, binary classifiers that draw a line of separation through a graph in order to classify different instances of data.

Deep learning is a subset of machine learning, which is itself a subset of the larger field of artificial intelligence research. Deep learning models have significant advantages over traditional machine learning models. The primary advantage of deep learning architectures is that they can extract the features of a data set by themselves, doing away with much of, although certainly not all, the time-consuming manual labeling of data.

Unsupervised learning algorithms are able to adapt to changes in a real-time environment. Deep learning architectures also have advantages over regular machine learning systems when implementing supervised learning algorithms.

Artificial neural networks excel at analyzing image data and drawing conclusions based off of the data, in a way that traditional machine learning techniques cannot. This enables the usage of image and video data. As an example, images of fields collected by drones can help identify the possible spread of pests or disease through a field when compared against images of diseased plants. Applications of deep learning models to AgTech include the deployment of artificial neural networks to detecting the spread of diseases like blight and rust throughout fields, as well as detecting the spread of weeds.

Image Recognition For AgTech

As an example of image recognition based on the neural networks for AgTech, images of crop fields can be fed into a deep learning system, training the system to recognize different plants. The classification system can then be used to determine selective areas of a field for herbicide treatment, cutting down on herbicide use and pollution as a byproduct. Computer vision algorithms are being developed to analyze images of crop fields and detect individual plants, distinguishing between weeds and desired crops. Some models have over 95% precision and recall on both weeds and crops of interest.

Computer vision algorithms are also being used to enhance the speed at which crop breeding is done, enabling higher-yield and disease resistant crops to be bred much quicker, sometimes cutting the time it takes to quantify crop traits by 75% or more. Image processing systems can pick up on phenotypic differences between plants with much greater speed than a human can. For example, by using a convolutional neural network to detect disease/rot in photos of crops, analysts can gain valuable insight into which crop specimens prove the most disease resistant. These algorithms can help cut food waste and improve productivity, as it is estimated that approximately 40% of the crop production in the world is wasted or lost due to different crop diseases.

The data gathered by the algorithms may also eventually be used to make predictive models. For instance, if a correlation can be established between phenotypic measurements that have been geotagged and the genotypic data of plants, predictions could be made about the best crop varieties for planting in given climate conditions and geographical locations.

As improvements in computer vision technology and deep learning techniques continue, the number of possible applications for the AgTech sector will grow as well.

In order to accomplish tasks such as the detection of diseased plants or the recognition of plant phenotypes, the AI system used for AgTech (or anything else) must be fed properly annotated and labeled data. Image annotation companies like Microwork specialize in the handling of annotation of image data, and outsourcing your image annotation tasks to qualified annotation experts will save you a lot of time and stress. Microwork will annotate your images with precision, speed, and quality, utilizing the most recent, powerful technologies along with professionally trained annotators.

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