Crop Yield Project

early Sample Videos

1. What is efficient farming?

Farmers frequently need to make challenging decisions (right decisions, right time, right place, , and right amount) that impact crop productivity, profitability, and sustainability. Making the right decision is often the difference between profit and loss. Farmers require hard numbers to allow them to rapidly make informed decisions. However, much of this information is collected through laborious manual observation, guesswork, or subjective data collection. For example, crop yield estimation can help producers, agronomists, breeders, researchers, insurance companies, policy makers, and governments to rapidly make informed management and financial decisions. Monitoring and estimating in-field crop yield in real-time is now required more frequently due to climate change. However, obtaining accurate yield estimates at the individual farm field level still rely on inaccurate, unpleasant, and time-consuming manual observation.

2. The current ‘quick and easy’ practice of crop yield estimation

Methods:  To estimate crop yield in a field, producers and agronomists randomly select and manually count the number of wheat heads from a specific length (e.g., 3 ft) of plant row in various spots around a field. By counting kernels on heads, determining the distance between rows, and applying each measure to a formula, yield can be approximated.

Problems: (1).  Crop yield estimation relies on unpleasant and time-consuming manual observation. (2). Rapidly estimating crop yield is challenged due to the time it takes and the difficulty in selecting sufficient representative samples. The sustained manual work causes miscounts, and fields are seldom uniform; drought usually accentuates field yield variability.

3. Super GeoAI Smartphone Solution

Solution: Users walk with a smartphone to take several photos per representative location around a field. Our mobile application can count and label wheat heads, and calculate crop yield in the photo within three seconds, with an accuracy approaching 100%. Once users take a certain number of photos, our app can give users approximately accurate crop yield estimation (such as bushels per acre) in a field.

The  smartphone photos will be first processed to calculate crop yield by our fast GeoAI algorithms in the smartphone. The photos will be automatically uploaded and processed by our advanced GeoAI algorithms in our cloud platform, provided that the internet is available. If the advanced algorithms have better results, it will update the local result. In addition, users can login to the cloud platform to visualize the accuracy and easily fix any AI mistakes to achieve 100% accuracy, if necessary.

(1). The following demo uses our mobile app prototype to take photos of wheat heads (about two rows and 3 feet length per photo). The app immediately calculates the crop yield in each photo and the average crop yield in the whole field. Click Here to download the original high-resolution video. You can select the Settings Gear Icon on the lower right-hand side of the player and Select the Quality drop down box that pops up to change YouTube  Video Quality Setting.

(2). The following demo uses the smartphone camera to take several photos per representative location around a field and uses our mobile app prototype to calculate crop yield in these photos at one time. Click Here to download the original high-resolution video. You can select the Settings Gear Icon on the lower right-hand side of the player and Select the Quality drop down box that pops up to change YouTube  Video Quality Setting.

4. Super GeoAI Drone Solution

Solution 1: While our mobile application can achieve approaching 100% accuracy for each photo, producers may be unable to randomly adequate representative samples throughout an non-uniform crop field.  Our GeoAI-driven drones have the ability to rapidly and randomly sample throughout a field, providing a higher level of assessment accuracy over walking and selecting spots manually in the field. Our GeoAI-driven drone can automatically take off from farmers’ backyard, fly to the crop fields and take photos. Upon task completion, the drone will automatically return to the starting location. The drone image (e.g., The file size of a DJI mini 2 drone image that covers 30sq ft is about 5 MB.) will be automatically uploaded and processed to calculate crop yield by our  GeoAI algorithms in our cloud platform, given that the internet and Wi-Fi is available. Users can also manually upload drone images into our cloud platform.

Users can upload drone images in our cloud platform to calculate the wheat crop yield in the images. Below is a demo video. Click on the three-dot icon at the lower right-hand side of the player to download the original high-resolution video. You can also play the reduced resolution video faster Here at YouTube.

Solution 2: Random sampling throughout a field provides higher assessment accuracy than walking and selecting spots in the field manually. However, fields are seldom uniform and drought usually accentuates field yield variability. Integrating the traditional vegetation indices and traditional crop yield prediction model into our GeoAI-driven models can create high-resolution estimated yield maps and growth stage maps of a field. We are developing our advanced GeoAI models for this solution.

The following drone image shows the variability of crop throughout a field, where some areas have a much higher wheat yield than others.

The following drone image shows the SGA’s own non-uniform wheat field in 2022. The areas with higher yield and later maturity have not yet been harvested. A drone can take photos of the areas about 30-100 meters above ground and our GeoAI algorithms can accurately calculate these polygon areas.

The following video shows that  the Dji Mini 2 drone is collecting wheat head data about 2-3 meters above ground. Click on the three-dot icon on the lower right-hand side of the player to download the original high-resolution video and play. You can also play the reduced resolution video faster Here at YouTube.

The following video (472 mb) shows the Dji Mini 2 collecting wheat data from 50 meters to 3 meters above the ground. Click Here to download the original high-resolution video. You can select the Settings Gear Icon on the lower right-hand side of the player and Select the Quality drop down box that pops up to change YouTube  Video Quality Setting.

5. Super GeoAI Crop Yield Prototype Testing and Validation

Our preliminary study in SGA’s wheat field has achieved 95%-100% accuracy.  In the following drone video and drone photos shows that SGA development team is testing and validating SGA’s crop yield prototypes in SGA’s wheat field in August 2022.

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