Machine learning approach in Irrigation
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I am new to machine learning. I am working on a project of Machine learning irrigation problem. I need to study on particular crop (ex. Rice crop). I have to apply the machine learning approach to tell the farmer on the basis of climatic parameters that seed need to sow or not (like should farmer water the field or not).
Rice need following parameter climatic condition: - on the average, about 180–300 mm water/month is needed to produce a reasonably good crop of rice. - Optimum temperature 20-35 degree celsius
My datsets link: https://github.com/TanvirMahmudEmon/Rainfall-Prediction/blob/master/data/final-dataset.csv
Here are my following doubts:
1) Is it falls under Supervised problem or Unsupervised problem (I think it lies under Classification Supervised problem) ?
2) How do I label the datasets for training purpose. (I think by doing if-else in python by comparing the temp field and rainfall filed by standard rice climatic valueand label accordingly yes or no ) ?
3) If I label according to my approach mentioned in step (2) . How I could do for whole datasets ?
4) Which ML algorithm I should try to gain more accuracy?
machine-learning scikit-learn dataset pandas machine-learning-model
asked 2 days ago
RatneshRatnesh
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I am new to machine learning. I am working on a project of Machine learning irrigation problem. I need to study on particular crop (ex. Rice crop). I have to apply the machine learning approach to tell the farmer on the basis of climatic parameters that seed need to sow or not (like should farmer water the field or not).
Rice need following parameter climatic condition: - on the average, about 180–300 mm water/month is needed to produce a reasonably good crop of rice. - Optimum temperature 20-35 degree celsius
My datsets link: https://github.com/TanvirMahmudEmon/Rainfall-Prediction/blob/master/data/final-dataset.csv
Here are my following doubts:
1) Is it falls under Supervised problem or Unsupervised problem (I think it lies under Classification Supervised problem) ?
2) How do I label the datasets for training purpose. (I think by doing if-else in python by comparing the temp field and rainfall filed by standard rice climatic valueand label accordingly yes or no ) ?
3) If I label according to my approach mentioned in step (2) . How I could do for whole datasets ?
4) Which ML algorithm I should try to gain more accuracy?
machine-learning scikit-learn dataset pandas machine-learning-model
asked 2 days ago
RatneshRatnesh
101
New contributor
Ratnesh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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Is your problem essentially to forecast the weather? Unless you really want to build a ML based weather forecasting model (which I suggest is re-inventing the wheel, as there are very good and free weather forecast API's available), I think your model needs to take a weather forecast as an input and generate a yield of rice as an output. To build that model, you would need training data of many rice crops and the weather during the rice's growth. The data you linked doesn't seem to have rice yield
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– macaw_9227
yesterday
add a comment |
$begingroup$
I am new to machine learning. I am working on a project of Machine learning irrigation problem. I need to study on particular crop (ex. Rice crop). I have to apply the machine learning approach to tell the farmer on the basis of climatic parameters that seed need to sow or not (like should farmer water the field or not).
Rice need following parameter climatic condition: - on the average, about 180–300 mm water/month is needed to produce a reasonably good crop of rice. - Optimum temperature 20-35 degree celsius
My datsets link: https://github.com/TanvirMahmudEmon/Rainfall-Prediction/blob/master/data/final-dataset.csv
Here are my following doubts:
1) Is it falls under Supervised problem or Unsupervised problem (I think it lies under Classification Supervised problem) ?
2) How do I label the datasets for training purpose. (I think by doing if-else in python by comparing the temp field and rainfall filed by standard rice climatic valueand label accordingly yes or no ) ?
3) If I label according to my approach mentioned in step (2) . How I could do for whole datasets ?
4) Which ML algorithm I should try to gain more accuracy?
machine-learning scikit-learn dataset pandas machine-learning-model
asked 2 days ago
RatneshRatnesh
101
New contributor
Ratnesh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am new to machine learning. I am working on a project of Machine learning irrigation problem. I need to study on particular crop (ex. Rice crop). I have to apply the machine learning approach to tell the farmer on the basis of climatic parameters that seed need to sow or not (like should farmer water the field or not).
Rice need following parameter climatic condition: - on the average, about 180–300 mm water/month is needed to produce a reasonably good crop of rice. - Optimum temperature 20-35 degree celsius
My datsets link: https://github.com/TanvirMahmudEmon/Rainfall-Prediction/blob/master/data/final-dataset.csv
Here are my following doubts:
1) Is it falls under Supervised problem or Unsupervised problem (I think it lies under Classification Supervised problem) ?
2) How do I label the datasets for training purpose. (I think by doing if-else in python by comparing the temp field and rainfall filed by standard rice climatic valueand label accordingly yes or no ) ?
3) If I label according to my approach mentioned in step (2) . How I could do for whole datasets ?
4) Which ML algorithm I should try to gain more accuracy?
machine-learning scikit-learn dataset pandas machine-learning-model
machine-learning scikit-learn dataset pandas machine-learning-model
asked 2 days ago
RatneshRatnesh
101
New contributor
Ratnesh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
RatneshRatnesh
101
New contributor
Ratnesh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
RatneshRatnesh
101
New contributor
Ratnesh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
RatneshRatnesh
101
asked 2 days ago
RatneshRatnesh
101
101
New contributor
Ratnesh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Ratnesh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Ratnesh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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Is your problem essentially to forecast the weather? Unless you really want to build a ML based weather forecasting model (which I suggest is re-inventing the wheel, as there are very good and free weather forecast API's available), I think your model needs to take a weather forecast as an input and generate a yield of rice as an output. To build that model, you would need training data of many rice crops and the weather during the rice's growth. The data you linked doesn't seem to have rice yield
$endgroup$
– macaw_9227
yesterday
add a comment |
$begingroup$
Is your problem essentially to forecast the weather? Unless you really want to build a ML based weather forecasting model (which I suggest is re-inventing the wheel, as there are very good and free weather forecast API's available), I think your model needs to take a weather forecast as an input and generate a yield of rice as an output. To build that model, you would need training data of many rice crops and the weather during the rice's growth. The data you linked doesn't seem to have rice yield
$endgroup$
– macaw_9227
yesterday
$begingroup$
Is your problem essentially to forecast the weather? Unless you really want to build a ML based weather forecasting model (which I suggest is re-inventing the wheel, as there are very good and free weather forecast API's available), I think your model needs to take a weather forecast as an input and generate a yield of rice as an output. To build that model, you would need training data of many rice crops and the weather during the rice's growth. The data you linked doesn't seem to have rice yield
$endgroup$
– macaw_9227
yesterday
$begingroup$
Is your problem essentially to forecast the weather? Unless you really want to build a ML based weather forecasting model (which I suggest is re-inventing the wheel, as there are very good and free weather forecast API's available), I think your model needs to take a weather forecast as an input and generate a yield of rice as an output. To build that model, you would need training data of many rice crops and the weather during the rice's growth. The data you linked doesn't seem to have rice yield
$endgroup$
– macaw_9227
yesterday
add a comment |
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Is your problem essentially to forecast the weather? Unless you really want to build a ML based weather forecasting model (which I suggest is re-inventing the wheel, as there are very good and free weather forecast API's available), I think your model needs to take a weather forecast as an input and generate a yield of rice as an output. To build that model, you would need training data of many rice crops and the weather during the rice's growth. The data you linked doesn't seem to have rice yield
$endgroup$
– macaw_9227
yesterday