SPACECROPS

Summary

Team Space-ial Circle believes self-sufficiency to be the most effective way of avoiding food disruption and covid-19 spread through foodstuff. We decided to increase the effectiveness of internal crop production in these times of pandemic, where resources are short and global co-operation is hindered. The application uses a large quantity of NASA and JAXA’s dataset to identify the factors of production of a soil in real time, compares it with the desired crop the farmer decides to cultivate.

How We Addressed This Challenge

SPACECROPS

SpaceCrops is an app developed keeping in pace with our motto of increasing efficiency to make countries self-sufficient.

What challenge does it address:

We are addressing 'Food for Thought' challenge. Our goal is to ensure nutritious food to properly make its way to our plates without contributing to the global pandemic spread. We observed the spreading through global food trade and decided to create mass production of crops internally. Through it, the spread of covid-19 through food can drastically be neutralized. 

How does it address the challenge:

We utilize both constant agricultural data set and satellite live data input. We allow the user to compare the respective optimum factors of production and the live values of them in the current soil. We aim to capitalize the use of nature in these times of shortage of resources. The app allows any individual to access separate datasets from NASA and JAXA and collect live information about factors like Soil Moisture, Drought Index etc. At the same time they can check our collected database to know the optimum range of the factors for the breed.

How does it work (Front End):

The user first will be asked of his location; this increases the accuracy of mapping. He will then  select the crop he intends to cultivate. Afterwards, he shall find a map from where he will have to choose the place of production and the radius of his land. He will then find a comparison between factors like moisture, temperature, pH of the given soil and the stated crop. He will also find a visual representation of live prediction of each factor at different times of the year.( this feature is not yet coded) and also find the relative efficiency of production at the soil. It will also give flood, rain and wildfire alerts.

How does it work (Back End):

Up to some extents, we completed our back end activities, however eventually it will be perfected. We use APIs to extract data from JAXA and NASA and integrate to the app to display live soil factors. We also use our pre-provided data set from CGIR for crop constants. Using APIs and constant data supply we use some plotting functions to even visualize the data and provide soil crop fit. We use an algorithm to calculate predicted efficiency. Emergency alerts also takes into account the APIs that will be provided in the later parts of the application

What impact it will have:

• It will increase crop production and thus contribute in global food sufficiency.

• It will reduce covid-19 spread through foods

• Pivoting a crop choice becomes risk free

• There will be updates before or at the early stages of flood, rain, wildfire etc.

• We can produce more foods using less resources liker fertilizers, water and labor.

• Planned agriculture is attained

How We Developed This Project

To be exactly honest we first chose "Purify the Air" challenge and came up with a decent solution. But something just wasn't settled within. It didn't take us long enough to realize, our solution was more sophisticated and have less impact. We wanted to create a space based app whose potential would not be limited to the competition. We expect higher impact and promise from our projects.

All of us are from an agrarian country with more than 80% people engaged in agriculture where farming still follows the traditional methods. Scarcity of food, price hike and natural disasters are quite observed here. Scholars predicted more people dying of food scarcity in the effects of COVID-19 than that of being affected by it in the country. This is a universal problem and despite amazing technological advances; use of satellite in farming by the common farmers is still rarely observed. We aim to ultimately shape this app in such UI that even a child can operate it as we will take all the hard work in the back end.

We divided the work load between each and every members and covered for each other's weaknesses. Instead of coming to a conclusion with a perfect plan and then hassling over data sets; we started to find, analyze and visualize data. And to be honest, this was the hardest part; we kept searching every resources for data sets, APIs, live broadcasts etc. starting from NASA to JAXXA to CNES. And something popped up in the mind of Adib Ahnaf, "What if we ease this process of searching, analyzing and visualizing the plethora of data to the common agrarian individuals?" Thus through our search for clues of solution; we found our desired problem. The work were distributed. Mahthir Shahriar was in data collection, Adib Ahnaf was in analysis and planning, Safwan Zaeem was creating visualization of the data, Azmain Hossain was in designing and presentation while Mushfiqur Rahman was in App Prototype Development.

We have been using a lot of APIs from NASA's earth dataset and JAXA. We also used open source data from BARI, BRRI and CGIR. We have created the app prototype on Adobe XD; did our code in JAVA through Android Studio. Future advanced Data Visualization will require Python.We will also integrate artificial image analysis with more sophisticated algorithms in the near future.

Project Demo

Here's the link to our demo video.

Our Current Progress:

We have built our prototype of features that if utilized can produce 1.5 times more crops. Our prototype mostly allows the access of many scattered dynamic space data and allows comparison with static crop data. It allows real time monitoring of small areas and provides warning and alerts in case of disasters. We also use algorithms to find soil-crop fit.

Our solution can increase global crop production by almost 50% if properly utilized. It can reduce crop pivoting costs to 0$ and is expected to save 18.5 million dollars worth of unexpected disaster crop loss.

Our Future Plans:

In future, we expect to use machine learning and neural links to analyze the data and set observational patterns. We also aim to use image processing to understand the physical basis of lands in addition to chemical. We aim to integrate that application with automated mechatronics to increase production by many folds. We have developed such components like fire fighting drones and plastic reduction rover and we expect using space-based data sets with artificial intelligence through well-managed coding can bring about the actual future of agriculture. Win or lose, this is a goal we hope to achieve.

Our estimation shows about 8 folds more production using space based data sets with AI and neural technologies.

Data & Resources

Data Use:

Time Period Based Data:

Precipitation, Soil Moisture, Drought Index, Land Water Index, Solar Radiation, Surface Temperature, Vegetation Index,Anomaly of the above mentioned: https://suzaku.eorc.jaxa.jp/JASMIN/index.html

Metrics:

Soil Moisture:

Red= 40-50%

Orange=30-40%

Yellow=20-30%

Green=10-20%

Blue=0-10%

#Similar metrics are mentioned for each factor in JASMIN's website

Real Time Data:

Rain Conditions: https://sharaku.eorc.jaxa.jp/GSMaP_NOW/

Rainfall Precipitation: https://sharaku.eorc.jaxa.jp/GSMaP_CLM/index.htm

Rainfall Forecast: https://sharaku.eorc.jaxa.jp/GSMaP_RNC/index.htm

Wind Conditions: https://www.eorc.jaxa.jp/theme/NEXRA/index.htm

Area based real-time factors (Soil Moisture, Soil Temperature, Solar Radiation, Vegetation Index, Precipitation): http://www.jpmap-jaxa.jp/Agri/index.html

Near Real Time Land Based Data: https://ldas.gsfc.nasa.gov/gldas

Production metrics and scarcity alerts: https://ldas.gsfc.nasa.gov/fldas

Real Time Disaster Alerts: 

Fire: , MYD14,MOD14, VNP14IMG_NRT, VJ114IMGTDL_NRT

Floods: https://earthdata.nasa.gov/earth-observation-data/near-real-time

#Partial integration of data from LANDSAT-8 was utilized but could not be classified.

Tags

#SPACECROPS #Food4Thought #Satellite4Farming #FutureofAgriculture

Global Judging

This project has been submitted for consideration during the Space Apps Global Judging process.