With the advancements in technology, the agriculture landscape is changing like never before. Today, agriculture is not only about harvesting crops and getting them to the market. It has moved much beyond that.
When we think of agriculture, we think of it as a field that’s lagging behind in implementing the latest technologies. However, that’s not the case because farmers are now quickly modernizing farming.
And guess with what? Artificial Intelligence and Machine Learning are involved. If this surprised you, read on to find out how agriculture chatbots are changing the farming game.
How is AI used in Agriculture?
AI is used in agriculture in more ways than one and the process of cultivation has changed with the advent of AI. Following are some of the applications of AI in agriculture.
- Sowing seeds – AI is used in predicting the best place for the seed to grow, the most favorable soil conditions, etc.
- Harvesting equipment – Agriculture businesses are leveraging AI-driven robotic harvesting equipment like driverless tractors
- Pest control – AI is opening doors for farmers to take care of the crops and keep them pest-free with the help of advanced AI technology and built-in computer vision.
- Airborne surveillance – UAVs and drones with precision sensors are deployed to gather the necessary airborne data. They also look for seeds, pest damage, and more. This process is optimized and sped up because it would be more time-consuming if it was done manually.
- Soil and crop health monitoring – Soil and nutrition play a key role in the quality of the crop. A German-based AI application, Plantix, can identify nutrient deficiencies in the soil. It can also suggest to the farmers what fertilizer to use to improve the crop quality using image recognition-based technology.
- Precision farming – Precision farming is yet another excellent application of Machine Learning in farming. AI-driven technology guides the farmers on water management, crop rotation, what crop is to be grown, pest attacks, nutrition management, and more.
- Weather forecasting – Sudden change in climatic conditions and the ever-rising pollution makes life difficult for farmers in determining the right seed to sow. Using AI, farmers can make wiser decisions on the right crop to grow at a given time.
- Chatbots – Chatbots are used by Agricultural companies to improve customer service and overall efficiency and productivity.
More on this in the further sections!
Why Does Your Agriculture Business Need a Chatbot?
A chatbot’s applications in enterprises are no secret in today’s rapidly evolving world. Chatbots act as virtual assistants for businesses. This helps serve the customers better and improve efficiency and productivity by focusing on the more important tasks.
Chatbots in agriculture help automate and streamline farming processes that would normally take hours. A chatbot can help your agriculture business in the following ways:
- Engage potential new clients – Agriculture chatbots could help you gather information about prospects that engage with the bot. You could thereby segment this information and reach out to them in a more targeted and effective way.
A great example of this is a well-constructed Shopify chatbot. A Shopify chatbot is similar to a sales executive in a store. Just like a sales executive, the chatbot can take care of product inquiries and other customer service related processes.
A high-level AI chatbot can do the trick for your agriculture business as well. More on building such chatbots in the further sections. Read on!
- Save your time – When the chatbot is at its work, you won’t need a human to manually respond to the customer inquiries and questions again and again. Using a chatbot instead of hiring someone to answer frequently asked questions will significantly save time, energy, and resources.
- Convenient communication – Facebook Messenger bots are used by businesses to make them feel comfortable with a conversational type of customer service. This can be observed in answering customer questions, promoting offers, and giving them all the necessary information they need to make a purchase.
How to Build a Chatbot for Agriculture Using Machine Learning?
Lots of chatbot-building tools are available in the market to help you ace your agriculture business chatbot game. But take note that the chatbot-building process might not be the same in all the tools equipped with Machine Learning. However, the procedure to build a chatbot for an agriculture business need not be the same throughout.
Before we jump into how to build an agriculture chatbot using Machine Learning, let’s first dive deep into the basics of deep learning
What is Deep Learning?
As the name suggests, a deep learning chatbot learns everything from scratch and builds that knowledge from continuous interaction. It learns what it can through processing the data fed to it and through the interactions it makes.
The biggest challenge with chatbots is to help them understand the statements that they are not trained for. However, current day chatbots are so well trained, that they have their own consciousness. They can therefore be trained with algorithms to converse with people.
With the basics of deep learning covered, let’s get right into the process of building your agriculture chatbot.
Step-by-Step Guide to Building an Agriculture Chatbot
Multiple deep learning approaches are adopted by developers to build chatbots. However, you would require an advanced method to minimize human management. Chatbots also have to be pretty advanced to come up with a bot that would understand complex customer intentions.
Given below is the starting guide to develop an agriculture chatbot with Machine Learning.
- Fetch the Data
The first and foremost step to building a chatbot is to acquire the data of interactions between the customers and your support team. This is also known as ontology. Try to get as many types of detailed interactions as possible so you have enough data for your bot. The objective of this stage is to gather as many interactions with customers as you possibly can.
- Data Sorting
If your data isn’t sorted into different observation rows, you might want to segregate and reshape it. The purpose of this step is to consider one speaker as a response in an interaction. The incoming dialogue would then be used as indicators to predict the response.
- Pre-Processing Data
This step involves training the deep learning bot on Grammar so that it can interpret the misspelled words correctly. Concepts like tokenizing, lemmatizing, and stemming are involved in this stage. This makes it easier for the bot to read the chat clearly.
- Select chatbot type
After pre-processing, the next step is to select the type of chatbot. Basically, there are two kinds of chatbots.
- Generative – doesn’t use any predefined repository of queries but rather uses deep learning to respond to queries
- Retrieval-based – has response repository to solve the queries, the customer chooses from the response options available, and the bot replies
- Word Vector Creation
Word vectors are created for frequently used words that aren’t included in most datasets. Some of the good examples are LMAO, ROFL, LOL, ASAP, etc. The pre-trained vectors don’t include such words and it’s always good to train your bot on the words that aren’t a part of word vectors.
- Seq2Seq Model
Next, you’ll have to create a Seq2Seq model using tools like TensorFlow. GitHub has a repository of Python scripts for this purpose. You might want to rope in a Python developer to create a code for your deep learning bot.
- Track the Training
This is the most exciting part of building an agriculture chatbot. Here, you get to see how the deep learning bot gets trained on various responses. Using an input string, you shall test the chatbot at different points.
- Publish Your Seq2Seq
Once you have created your Seq2Seq and tested it, it’s time you publish it to an application and let customers interact with it. You can get started with this on Node by creating a new folder and starting a new Node project.
- Deploy TensorFlow
As soon as your bot is launched on an application like Facebook Messenger, you shall integrate TensorFlow and Node. A Flask Server would be the best choice to deploy your TensorFlow model considering the limited options available.
- Test Your Chatbot
Finally, it’s time to test your agriculture chatbot live. Head to the application where you launched your bot and start interacting with it. Note the responses of the chatbot to your messages as this would act as feedback for further improvements.
- Improve Your Chatbot
Your interactions with the chatbot during testing would pave the way for this step. To take your bot’s performance to the next level, you can do the following:
- Add more datasets
- Double-check the encoder and decoder messages
- Tune hyperparameters
- Use bucketing and attention mechanisms
Building a bot for your agriculture business is fun. Although chatbot building requires coding and programming knowledge, certain tools enable you to build chatbots with no coding experience whatsoever. So if you’re a newbie to Python and coding, fret not as these tools have got you covered.