Farmers have always been the back-bone of our country & the NDA Government is striving to strengthen this back-bone of the country through innovative and solid measures.
As we know that Agriculture is a major contributor to the economy. The mainstream Indian population depends either explicitly or implicitly on agriculture for their livelihood. It is, thus, irrefutable that agriculture plays a vital role in the country. A vast majority of the Indian farmers believe in depending on their intuition to decide which crop to sow in a particular season.
We know that a farmer’s decision about which crop to grow is generally clouded by his intuition and other irrelevant factors like making instant profits, lack of awareness about market demand, overestimating a soil’s potential to support a particular crop, and so on. A very misguided decision on the part of the farmer could place a significant strain on his family’s financial condition.
Perhaps this could be one of the many reasons contributing to the countless suicide cases of farmers that we hear from media on a daily basis.
In a country like India, where agriculture and related sectors contribute to approximately 20.4 percent of its Gross Value Added (GVA) , such an erroneous judgment would have negative implications on not just the farmer’s family, but the entire economy of a region. For this reason, we have identified a farmer’s dilemma about which crop to grow during a particular season, as a very grave one.
The need of the hour is to design a system that could provide predictive insights to the Indian farmers,thereby helping them make an informed decision about which crop to grow.
With this in mind, we propose Farmo-Consultant- an intelligent system that would consider environmental parameters (temperature, rainfall) and soil characteristics.
What it does
We have successfully proposed and implemented an intelligent crop recommendation system, which can be easily used by farmers all over India. This system would assist the farmers in making an informed decision about which crop to grow depending on a variety of environmental and geographical factors. The proposed system takes into consideration the data related to soil, weather and past year production and suggests which are the best profitable crops which can be cultivated in the apropos environmental condition. As the system lists out all possible crops, it helps the farmer in decision making of which crop to cultivate. Also, this system takes into consideration the past production of data which will help the farmer get insight into the demand and the cost of various crops in market. As maximum types of crops will be covered under this system, farmer may get to know about the crop which may never have been cultivated.
How we built it
Firstly we collected the dataset for our project then we started Data Pre-Processing :
This is a two-step process. The first step is to remove the missing values which were represented by a dot (‘.’) in the original dataset. The presence of these missing values deteriorates the value of the data and subsequently hampers the performance of machine learning models. Hence, in order to deal with these missing values, we replace them with large negative values, which the trained model can easily treat as outliers. The second step before the data is ready to be applied to machine learning algorithms is to generate class labels. Since we intend to use supervised learning, class labels are necessary. The original dataset did not come with labels, and hence we had to create them during the data preprocessing phase.
Applyed Machine Learning Algorithms : Since in the proposed model, more than one class can be assigned to a single instance, Multi-label classification (MLC) would be the ideal choice. Decision Tree, K Nearest Neighbor (K-NN), Random Forest and Naive Bayes are four machine learning algorithms that have in-built support for MLC. We also tried different Machine Learning Algorithms but we got some less accuracy only in the decision tree we got 80.65%, using Random forest we got (74.62), KNN(71.12), Naive Bayes(65.02) of accuracy
Trained Model and Crop Recommendations : After applying the data to different machine learning algorithms, we obtain trained models of the crop recommendation system. The weights of this model can then be saved, and the farmers can easily avail crop recommendations by giving their farm’s Soil type, Rainfall, temperature, Ground water availability and season as the input to the system.
Deployment: Deployment of an ML-model simply means the integration of the model into an existing production environment which can take in an input and return an output that can be used in making practical business decisions. So we used flask and flask is a web framework. flask provides tools, libraries, and technologies that allow us to build a web application.
Challenges we ran into
We faced a problem at the starting of our project. In the starting we unable to find enough data set but after search more & more we got the data set. The second problem we faced is in finding the max accuracy. We tried different Machine Learning Algorithms but we got somewhat less accuracy only in the decision tree we got a max of 80.65% accuracy.
Accomplishments that we're proud of
We are able to create a smart farming system for farmers that can benefit them throughout their life using this system that can grow the best suitable crop so that they can get maximum output through their crops and numerically as well as financially. So finally, We have successfully proposed and implemented an intelligent crop recommendation system, which can be easily used by farmers all over India. This system would assist the farmers in making an informed decision about which crop to grow depending on a variety of environmental and geographical factors.
What we learned
While doing this project we learned a lot of things. the very first one is that every problem has one solution but for this, we only have to focus on finding the right solution for that. As this project is based on machine learning we get a chance to learn more about machine learning algorithms. we learn about flask.
What's next for Farmo-Consultant
Future Aspects :
Alternate Crops Recommendation System:
Artificial Intelligence (AI) based Alternate Crop or Crop Rotation proposition is desired for providing suggestions for alternate crops which may increase profitability of the farmers by increasing the crop yield and maintaining the fertility of soil .
Additionally, CHATBOTS can be introduced along with this system which is a conversational virtual assistant and provides farmers the better opportunity to obtain the desired information and to scale up with upcoming market trends and technologies in a user friendly manner.
Expanding this system globally:
As this crop recommendation system is limited to benefit Indian Farmers only, therefore, we can also expand the scope of this project globally by training our model with a dataset of all the different types of location in the world where different crops are grown.