Prominent Applications of Deep Learning in Agriculture

Prominent Applications of Deep Learning in Agriculture

In this post on Prominent Applications of Deep Learning in Agriculture, I will discuss 10 significant application scenarios. In fact, all such applications can greatly benefit from Deep Learning Techniques.

What is Deep Learning?

Deep Learning is a subset of machine learning. Mainly Deep Learning comprises techniques that take inspiration from the way how the human brain works. In other words, in this branch of Machine Learning, the learning is done using Artificial Neural Networks (ANN). Also, we can have ANNs with several layers called Deep Neural Networks (DNN).

Applications of Deep Learning in Agriculture

As a matter of fact, there are several Deep Learning (DL) techniques. These include Artificial Neural Networks (ANN), Backpropagation, Deep Neural Networks (DNN). Additionally, we also have Convolution Neural Networks (CNN), Autoencoders, Reinforcement Learning, and so on. In fact, we can use many Deep Learning techniques to ou benefit in many real-life situations. Particularly, in agriculture, we can use AI and Deep Learning for improving the productivity and quality of farm produce. The following section discusses a number of significant applications of Deep Learning in the Agriculture sector.

Deep Learning Enabled Weed Detection

When we have lots of data to train, the Deep Learning works well in such applications. Also, for image classification problem, Deep Learning techniques can extract complicated information from input images.

In the case of a weed detection problem, we can train a model using the dataset consisting of images of the weeds. Further, we can use this dataset to distinguish weeds from crops.

Smart Greenhouse

Greenhouse automation is another application area where we can use AI and deep learning. Basically, a Greenhouse enables the independent climate inside. In the first place, managing microclimate for a greenhouse is quite complex. In fact, it requires managing several external factors such as temperature and humidity, the intensity of light, and wind direction. Of course, there are internal factors to manage also such as heating and ventilation. Therefore, it is worthwhile to use Artificial Neural Networks (ANN) to make predictions about the outcome. The ANN performs this task by modeling these many factors.

Hydroponics Using Deep Learning

Whenever the outcome depends on many input parameters, the problem is best suited for the application of Artificial Neural Networks (ANN). Indeed, maintaining a hydroponic system is one such application. Basically, a hydroponic system grows plants in a nutrient solution rather than soil.

In fact, there are many variables that affect the outcome of a hydroponic system including sunlight, temperature, pH balance, and nutrition density. Further, a Deep Neural Network (DNN) trains a model on the basis of input parameters in order to predict the appropriate control action.

Soil Nutrient Monitoring

The soil fertility is determined by the Organic Matter present in the soil which in turn determines the yield and the quality of produce. Accordingly, we can make use of Deep Learning Frameworks to predict the Soil Organic Matter in order to determine the soil fertility so that farmers can know the health and productivity of the soil of their farms.

Applications of Deep Learning in Agriculture
Applications of Deep Learning in Agriculture

Crop Yield Prediction

The crop yield prediction is another application where we can utilize Deep Learning Techniques such as Deep Neural Networks (DNN). Evidently, crop yield has been effecting by several factors. Hence, we can use a DNN in such a case. Moreover, a complex non-linear relationship exists between these factors. There are already many startups working in this direction.

Smart Irrigation

A Plant Recognition System based on Deep Learning can determine the water requirement for a particular plant type and can control the amount of water flow. Basically, a Convolution Neural Network (CNN) can be trained using the image dataset of various plants for recognizing the plant type.

Livestock Monitoring

As another application of Deep Learning, precision livestock farming can use a Deep Learning framework for individual cattle identification. In fact, there are a number of applications in livestock management that can utilize the model built using the dataset of images of various farm animals.

Therefore, it is possible to find the count of each type of cattle and their feeding behavior using images and videos captured by drones. Also, the onset of disease can be identified early in an animal before it spreads in other animals. Additionally, the use of drones is also very helpful in livestock tracking.

Fruit and Vegetable Plucking

Likewise, it is possible to create an image dataset of fruits and vegetables that we want a Deep Learning model to train. Further, we can employ the trained model in a fruit plucking robot that can distinguish the specific fruit from other objects and then pluck it. Particularly, we can use a Convolution Neural Network (CNN) for this purpose.

Early Detection of Plant Diseases

Another important application of Deep Learning in agriculture is the early detection of plant diseases. Similar to other deep learning applications, it also requires an image dataset of plant leaves with a particular disease.

Likewise, we can use a deep convolution neural network for training with the image dataset and a deep learning framework such as Caffe can be used to perform this task.

Summary

In this article on Prominent Applications of Deep Learning in Agriculture, I have discussed some of the significant functions of agriculture which can be greatly benefitted by using the techniques of Deep Learning. Evidently, the applications discussed above either require analyzing the complex non-linear relationship among several input parameters or the input data is available in the form of images. Therefore, these applications are suitable candidates for deep learning applications.


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