Inferring moisture from color
Techniques are described herein for using artificial intelligence to predict crop yields based on observational crop data. A method includes: obtaining a first digital image of at least one plant; segmenting the first digital image of the at least one plant to identify at least one seedpod in the first digital image; for each of the at least one seedpod in the first digital image: determining a color of the seedpod; determining a number of seeds in the seedpod; inferring, using one or more machine learning models, a moisture content of the seedpod based on the color of the seedpod; and estimating, based on the moisture content of the seedpod and the number of seeds in the seedpod, a weight of the seedpod; and predicting a crop yield based on the moisture content and the weight of each of the at least one seedpod.
1. A method implemented by one or more processors, the method comprising:
receiving training data including a digital image of a live plant in a field, wherein the digital image is labeled based on a ground truth moisture content of a first seedpod of a first live plant;
generating preprocessed training data using the training data by:
segmenting the digital image to identify the first seedpod of the first live plant in the digital image;
determining a color of the first seedpod; and
determining a number of seeds in the first seedpod;
training one or more machine learning models to predict one or both of a moisture content of the first seedpod and a weight of the first seedpod based on the preprocessed training data and the ground truth moisture content; and
predicting a crop yield of a second seedpod of a second live plant based on a predicted moisture content of the first seedpod and a predicted weight of the first seedpod.
2. The method of claim 1 , wherein the generating the preprocessed training data further includes determining a size of each of the first seedpod in the digital image.
3. The method of claim 1 , wherein the weight of the first seedpod is a wet weight.
4. The method of claim 1 , wherein one or more of the machine learning models is a convolutional neural network model.
5. The method of claim 1 , wherein the digital image is obtained using a multi-camera array.
6. The method of claim 5 , wherein the digital image includes a plurality of digital images obtained at a plurality of positions along a length of a row of plants.
7. At least one non-transitory computer-readable storage medium comprising program instructions to cause at least one processor to:
receive training data including a digital image of a live plant, wherein the digital image is labeled based on a ground truth moisture content of a first seedpod of a first live plant;
generate preprocessed training data using the training data, wherein to generate the preprocessed training data includes to:
segment the digital image to identify the first seedpod of the first live plant in the digital image;
determine a color of the first seedpod; and
determine a number of seeds in the first seedpod;
train one or more machine learning models to predict one or both of a moisture content of the first seedpod and a weight of the first seedpod based on the preprocessed training data and the ground truth moisture content; and
predict a crop yield of a second seedpod of a second live plant based on a predicted moisture content of the first seedpod and a predicted weight of the first seedpod.
8. The at least one non-transitory computer-readable storage medium of claim 7 , wherein to generate the preprocessed training data further includes instructions to determine a size of each of the first seedpod in the digital image.
9. The at least one non-transitory computer-readable storage medium of claim 7 , wherein the weight of the first seedpod is a wet weight.
10. The at least one non-transitory computer-readable storage medium of claim 7 , wherein one or more of the machine learning models is a convolutional neural network model.
11. The at least one non-transitory computer-readable storage medium of claim 7 , wherein the digital image is obtained using a multi-camera array.
12. The at least one non-transitory computer-readable storage medium of claim 11 , wherein the digital image includes a plurality of digital images obtained at a plurality of positions along a length of a row of plants.
13. A system comprising:
a processor;
one or more computer-readable storage media; and
program instructions stored on the one or more computer-readable storage media, the program instructions to cause the processor to:
receive training data including a digital image of a live plant in a field, wherein the digital image is labeled based on a ground truth moisture content of a first seedpod of a first live plant;
generate preprocessed training data using the training data, wherein to generate the preprocessed training data using the training data includes to:
segment the digital image to identify the first seedpod of the first live plant in the digital image;
determine a color of the first seedpod; and
determine a number of seeds in the first seedpod;
train one or more machine learning models to predict one or both of a moisture content of the first seedpod and a weight of the first seedpod based on the preprocessed training data and the ground truth moisture content; and
predict a crop yield of a second seedpod of a second live plant based on a predicted moisture content of the first seedpod and a predicted weight of the first seedpod.
14. The system of claim 13 , wherein, to generate the preprocessed training data, the program instructions are to cause the processor to determine a size of each of the first seedpod in the digital image.
15. The system of claim 13 , wherein the weight of the first seedpod is a wet weight.
16. The system of claim 13 , wherein one or more of the machine learning models is a convolutional neural network model.
17. The system of claim 13 , wherein the digital image is obtained using a multi-camera array.
18. The system of claim 17 , wherein the digital image includes a plurality of digital images obtained at a plurality of positions along a length of a row of plants.