Optimizing-Agriculture-Production

Optimizing-Agricultural-Production

Problem Statement

To predict the best cultivable crop for Suitable land and whether

Description for each of the columns in the Dataset

• N - ratio of Nitrogen content in soil
• P - ratio of Phosphorous content in soil
• K - ration of Potassium content in soil
• temperature - temperature in degree Celsius
• humidity - relative humidity in %
• ph - ph value of the soil
• rainfall - rainfall in mm

Descriptive Statistics

• Average Ratio of Nitrogen in the Soil : 50.55
• Average Ratio of Phosphorous in the Soil : 53.36
• Average Ratio of Potassium in the Soil : 48.15
• Average Tempature in Celsius : 25.62
• Average Relative Humidity in % : 71.48
• Average PH Value of the soil : 6.47
• Average Rainfall in mm : 103.46

Analyzing Agricultural Conditions

Some Interesting Patterns

• Crops which requires very High Ratio of Nitrogen Content in Soil: [‘cotton’]
• Crops which requires very High Ratio of Phosphorous Content in Soil: [‘grapes’ ‘apple’]
• Crops which requires very High Ratio of Potassium Content in Soil: [‘grapes’ ‘apple’]
• Crops which requires very High Rainfall: [‘rice’ ‘papaya’ ‘coconut’]
• Crops which requires very Low Temperature : [‘grapes’]
• Crops which requires very High Temperature : [‘grapes’ ‘papaya’]
• Crops which requires very Low Humidity: [‘chickpea’ ‘kidneybeans’]
• Crops which requires very Low pH: [‘mothbeans’]

• Crops which requires very High pH: [‘mothbeans’]

• Summer Crops [‘pigeonpeas’ ‘mothbeans’ ‘blackgram’ ‘mango’ ‘grapes’ ‘orange’ ‘papaya’] ———————————–
• Winter Crops [‘maize’ ‘pigeonpeas’ ‘lentil’ ‘pomegranate’ ‘grapes’ ‘orange’] ———————————–
• Rainy Crops [‘rice’ ‘papaya’ ‘coconut’] ———————————–

Clustering Similar Crops

Lets check the Results After Applying the K Means Clustering Analysis

Crops in First Cluster: [‘maize’ ‘banana’ ‘watermelon’ ‘muskmelon’ ‘papaya’ ‘cotton’ ‘coffee’]

Crops in Second Cluster: [‘grapes’ ‘apple’] ————————————————————— Crops in Third Cluster: [‘maize’ ‘chickpea’ ‘kidneybeans’ ‘pigeonpeas’ ‘mothbeans’ ‘mungbean’ ‘blackgram’ ‘lentil’ ‘pomegranate’ ‘mango’ ‘orange’ ‘papaya’ ‘coconut’] ————————————————————— Crops in Forth Cluster: [‘rice’ ‘pigeonpeas’ ‘papaya’ ‘coconut’ ‘jute’ ‘coffee’] —————————————————————

Results for Hard Clustering

Crops in Cluster 1: [‘cotton’, ‘watermelon’, ‘banana’, ‘muskmelon’, ‘maize’]

Crops in Cluster 2: [‘grapes’, ‘apple’] ————————————————– Crops in Cluster 3: [‘pomegranate’, ‘kidneybeans’, ‘chickpea’, ‘mango’, ‘orange’, ‘lentil’, ‘blackgram’, ‘mungbean’, ‘mothbeans’] ————————————————– Crops in Cluster 4: [‘coconut’, ‘papaya’, ‘jute’, ‘pigeonpeas’, ‘rice’, ‘coffee’] ————————————————–

visualizing the Hidden Patterns

Predictive Modelling

Real time Predictions

• For prediction = model.predict((np.array([[90, 40, 40, 20, 80, 7, 200]])))
• The Suggested Crop for Given Climatic Condition is : [‘rice’]