Systems and methods for crop health monitoring, assessment and prediction
Systems and methods for monitoring and assessing crop health and performance can provide rapid screening of individual plants. The systems and methods have an automated component, and rely primarily on the detection and interpretation of plant-based signals to provide information about crop health. In some cases knowledge from human experts is captured and integrated into the automated crop monitoring systems and methods. Predictive models can also be developed and used to predict future health of plants in a crop.
1. A method for assessing a state of plants in a crop, the method comprising:
in a training phase:
receiving a human expert assessment of a state of each plant of a first plurality of plants, wherein the human expert assessment is based on visual inspection of each plant of the first plurality of plants by a human expert;
receiving training sensor data captured for each plant of the first plurality of plants, the training sensor data related to at least one plant-related parameter, the training sensor data comprising sensor data from a device other than an illumination-based sensor; and
correlating the human expert assessment with the training sensor data using supervised learning to generate a set of trained data and a data-derived model based on the set of trained data; and
in an assessment phase:
receiving crop assessment sensor data captured for each plant of a second plurality of plants in the crop, the crop assessment sensor data related to at least one plant-related parameter, at least part of the crop assessment sensor data comprising sensor data from at least one device other than an illumination-based sensor;
classifying a state of each plant of the second plurality of plants by applying the data-derived model to the crop assessment sensor data; and
transmitting information relating to the state of each plant of the second plurality of plants to at least one end-user device;
wherein the first plurality of plants comprises plants that are healthy and plants that are exhibiting one or more particular problems, the human expert assessment based on visual inspection of the plants that are healthy and of the plants that are exhibiting the one or more particular problems, wherein the training sensor data is captured for each of the plants that are healthy and for each of the plants that are exhibiting the one or more particular problems; and
wherein the supervised learning comprises establishing statistical relationships between observed data that includes the training sensor data with truth data that includes the human expert assessment.
2. The method of claim 1 , wherein the crop comprises the first plurality of plants.
3. The method of claim 1 , wherein, in the training phase, the human expert assessment and the training sensor data are captured and transmitted by a hand-held sensory device operated by the human expert.
4. The method of claim 1 , wherein the at least one end-user device comprises a hand-held device.
5. The method of claim 1 , wherein, in the assessment phase, at least some of the crop assessment sensor data is captured and transmitted by a mobile sensory platform comprising at least one non-imaging sensor configured to be positioned proximate to each plant of the second plurality of plants during capture of the crop assessment sensor data for that plant.
6. The method of claim 1 , wherein, in the assessment phase, at least some of the crop assessment sensor data is captured and transmitted by a hand-held device operated by a worker at a crop-site where the crop is being grown.
7. The method of claim 1 , wherein:
the first plurality of plants comprises first, second, and third groups of plants; and
receiving the human expert assessment of the state of each plant of the first plurality of plants comprises:
receiving an assessment by a first human expert of a state of each plant of the first group of plants;
receiving an assessment by a second human expert of a state of each plant of the second group of plants; and
receiving an assessment by a third human expert of a state of each plant of the third group of plants.
8. The method of claim 7 , wherein the first, second, and third groups of plants are at different crop-sites.
9. The method of claim 8 , wherein the assessment by the first human expert of the state of each plant of the first group of plants is received at a different time than the assessment by the second human expert of the state of each plant of the second group of plants and at a different time than the assessment by the third human expert of the state of each plant of the third group of plants.
10. The method of claim 1 , wherein:
receiving the human expert assessment of the state of each plant of the first plurality of plants comprises recording and receiving verbal assessment of the state of each plant of the first plurality of plants; and
correlating the human expert assessment with the training sensor data to generate the set of trained data and the data-derived model comprises using natural language processing.
11. A crop monitoring and assessment system comprising:
a database configured to receive and store:
a human expert assessment of a state of each plant of a first plurality of plants, wherein the human expert assessment is based on visual inspection of each plant of the first plurality of plants by a human expert; and
training sensor data captured for each plant of the first plurality of plants, the training sensor data comprising sensor data from a device other than an illumination-based sensor;
a data processing unit communicatively coupled to the database, the data processing unit configured to generate a set of trained data and a data-derived model based on correlation of the human expert assessment with the training sensor data using supervised learning; and
a mobile sensory platform comprising a plurality of sensors configured to capture crop assessment sensor data for each plant of a second plurality of plants, at least part of the crop assessment sensor data comprising sensor data captured from at least one device other than an illumination-based sensor, the data processing unit communicatively coupled to the mobile sensory platform and further configured to (i) receive the crop assessment sensor data therefrom, (ii) classify the crop assessment sensor data for each plant of the second plurality of plants based on the data-derived model, and (iii) generate crop assessment information; and
a communication interface configured to transmit the crop assessment information to an end-user device;
wherein the first plurality of plants comprises plants that are healthy and plants that are exhibiting one or more particular problems, wherein the human expert assessment is based on visual inspection of the plants that are healthy and of the plants that are exhibiting the one or more particular problems, wherein the training sensor data is captured for each of the plants that are healthy and for each of the plants that are exhibiting the one or more particular problems; and
wherein, during the supervised learning, the data processing unit is configured to establish statistical relationships between observed data that includes the training sensor data with truth data that includes the human expert assessment.
12. The crop monitoring and assessment system of claim 11 , wherein the data processing unit is communicatively coupled to the mobile sensory platform over a wireless network.
13. The crop monitoring and assessment system of claim 11 , further comprising:
a hand-held sensory device configured to capture the training sensor data and transmit the human expert assessment and the training sensor data.
14. The crop monitoring and assessment system of claim 11 , wherein the plurality of sensors comprises physiological sensors, surface analysis sensors, and chemical sensors.
15. The crop monitoring and assessment system of claim 11 , wherein:
the first plurality of plants comprises first, second, and third groups of plants; and
the human expert assessment comprises:
an assessment by a first human expert of a state of each plant of the first group of plants;
an assessment by a second human expert of a state of each plant of the second group of plants; and
an assessment by a third human expert of a state of each plant of the third group of plants.
16. The crop monitoring and assessment system of claim 15 , wherein the first, second, and third groups of plants are at different crop-sites.
17. The method of claim 1 , wherein:
the training sensor data comprises sensor data from at least one of a physiological sensor, a surface analysis sensor, or a chemical sensor; and
the crop assessment sensor data comprises sensor data from at least one of a physiological sensor, a surface analysis sensor, or a chemical sensor.
18. The crop monitoring and assessment system of claim 11 , wherein:
the training sensor data comprises sensor data from at least one of a physiological sensor, a surface analysis sensor, or a chemical sensor; and
the crop assessment sensor data comprises sensor data from at least one of a physiological sensor, a surface analysis sensor, or a chemical sensor.
19. The method of claim 1 , wherein:
the data-derived model comprises a predictive model; and
applying the data-derived model to the crop assessment sensor data comprises predicting a future health or performance of each plant of the second plurality of plants based on the crop assessment sensor data.
20. An apparatus comprising:
at least one interface configured to receive:
a human expert assessment of a state of each plant of a first plurality of plants, wherein the human expert assessment is based on visual inspection of each plant of the first plurality of plants by a human expert;
training sensor data captured for each plant of the first plurality of plants, the training sensor data related to at least one plant-related parameter, the training sensor data comprising sensor data from a device other than an illumination-based sensor; and
crop assessment sensor data captured for each plant of a second plurality of plants in the crop, the crop assessment sensor data related to at least one plant-related parameter, the crop assessment sensor data comprising sensor data from a device other than an illumination-based sensor; and
at least one processor configured to:
correlate the human expert assessment with the training sensor data using supervised learning to generate a set of trained data and a data-derived model based on the set of trained data;
classify a state of each plant of the second plurality of plants using the data-derived model; and
initiate transmission of information relating to the state of each plant of the second plurality of plants to at least one end-user device;
wherein the first plurality of plants comprises plants that are healthy and plants that are exhibiting one or more particular problems, wherein the human expert assessment is based on visual inspection of the plants that are healthy and of the plants that are exhibiting the one or more particular problems, wherein the training sensor data is captured for each of the plants that are healthy and for each of the plants that are exhibiting the one or more particular problems; and
wherein, during the supervised learning, the at least one processor is configured to establish statistical relationships between observed data that includes the training sensor data with truth data that includes the human expert assessment.
21. The method of claim 1 , wherein the human expert assessment comprises, for each plant of the first plurality of plants, a human-assessed ranking of a level, from among a plurality of levels, that the plant is suffering from each of multiple problems.