CropMates
CropMates: A low-cost and efficient IoT and Al-based integrated system for the detection and treatment of crop diseases and deficiencies that maximize the quality and quantity of crops to potentially eradicate world hunger
CropMates: A low-cost and efficient IoT and Al-based integrated system for the detection and treatment of crop diseases and deficiencies that maximize the quality and quantity of crops to potentially eradicate world hunger
The world population is expected to increase by 2 billion by 2050. A person dies of hunger every 3.6 seconds. The UN has come up with a list of 17 goals to make the world a better place and ending hunger comes at second. A 119% increase in yield is required by 2050 to sustain life. As the amount of land cannot be increased, the only way is by increasing crop yield from the same land. Research has shown that disease and nutrient deficiency-related yield losses have cost the world 60% of global agricultural productivity. Diagnosis of the disease and nutrient deficiency in a large farm is very difficult because of the amount of crops. This project, “CropMates”, aims to identify crop diseases in a vast area of farmland by first surveying the acreage using a GPS-enabled drone. The camera-fitted drone captures video and pictures of the farm while flying over the field. A deep learning algorithm is created to identify leaves from these pictures. Another deep learning-based algorithm using AlexNet architecture is trained with 38 different classes of diseases on 87,000 images of leaves to identify a disease from these leaves. An Artificial Neural Network is developed and is trained on 20,000 data points to identify nutrient deficiencies in the soil from IoT enabled sensors put across the farm. An app is built to show the results and to recommend the type and amount of pesticide and fertilizer for optimum crop yield.
The world population is expected to increase by 2 billion by 2050. A person dies of hunger every 3.6 seconds. The UN has come up with a list of 17 goals to make the world a better place and ending hunger comes at second. A 119% increase in yield is required by 2050 to sustain life. As the amount of land cannot be increased, the only way is by increasing crop yield from the same land. Research has shown that disease and nutrient deficiency-related yield losses have cost the world 60% of global agricultural productivity. Diagnosis of the disease and nutrient deficiency in a large farm is very difficult because of the amount of crops. This project, “CropMates”, aims to identify crop diseases in a vast area of farmland by first surveying the acreage using a GPS-enabled drone. The camera-fitted drone captures video and pictures of the farm while flying over the field. A deep learning algorithm is created to identify leaves from these pictures. Another deep learning-based algorithm using AlexNet architecture is trained with 38 different classes of diseases on 87,000 images of leaves to identify a disease from these leaves. An Artificial Neural Network is developed and is trained on 20,000 data points to identify nutrient deficiencies in the soil from IoT enabled sensors put across the farm. An app is built to show the results and to recommend the type and amount of pesticide and fertilizer for optimum crop yield.
World Population Increases rapidly
To sustain the increased population the farm output needs to increase by 119% indicating higher yield from the same amount of land.
To sustain the increased population the farm output needs to increase by 119% indicating higher yield from the same amount of land.
Introducing CropMates
Introducing CropMates
Drone + Sensors + App + Algorithm
Drone + Sensors + App + Algorithm
Disease Detection Workflows
Disease Detection Workflows
Detection Algorithms
Detection Algorithms
Nutrient Detection in the Soil Workflow
Nutrient Detection in the Soil Workflow
Results
Results
App Screens
App Screens
