Recipient Organization
AGROFOCAL TECHNOLOGIES, INC
3598 COUR DE JEUNE
SAN JOSE,CA 951484306
Performing Department
(N/A)
Non Technical Summary
Agriculture is fundamental to our existence. As our world grows, agriculture productivity must increase to keep pace without reducing profits or increasing environmental impact. This requires continuous optimization of agriculture processes. However, we cannot optimize something that we cannot measure accurately. Crop monitoring is an essential activity that provides such measurements on crop status.Crop monitoring involves checking on the crops for any plant health issues, for estimating the produce, for planning labor deployment, or for other farming decisions. Traditionally this is done manually with experienced farmers walking their fields and observing the crops. Clearly with many hundreds of acres of farms, it is impossible to do this manually; only a small fraction of the fields can be surveyed in this fashion. Several companies are working to solve this problem using technology, but their solutions don't fit well with prevalent farming practices. Manyusedrones, aircrafts, or satellite images, which workwell forproviding broad overview of farms, but fail to look under the canopy and observe issues that require per-plant pictures. Many require images of the fields to be uploadedonto a central serverbefore they can be processed.The step of uploading images to a central server is time-consuming and, in many cases, requires manual oversight to complete. This approach prevents crop monitoring from being real-time. In addition, the amount of data that is required to be uploaded limits the size of the farm area that can be surveyed. Many companies provide crop monitoring as a service, where they dothe task of image collection and processing and then provide a report. In such cases, their monitoringschedule may or may not match with what is required by the growers. If a grower needs to do the monitoring every day orweek for every farm they own, it is usually extremely difficult for suchcompanies to provide that level of service at a reasonable cost.Agrofocal's goal is to build a real-time crop monitoring system that is easy to use, is affordable, and fits seamlessly with the existing farming operations. The crop monitoring system can be mounted on any moving vehicle going through the farm. This allows the camera to get up-close, under-the-canopy view of the crop. The entire process of collecting the images and operating on those images to extract the insights is done on the vehicle itself in real-time, which eliminates the time-consuming process of uploading the images to a central server for processing. The insights are available and ready to be viewed on an app as soon as the image collection is done. Since in this technology the image collection and the processing happen together without any manual intervention, it can be completely owned and operated by the grower themselves, without requiring the involvement of the company providing the technology. This way the grower can mount the system on their vehicle and do the monitoring as many times as they require. They can also choose to mount this system on a vehicle that would be going through the farm to perform some other tasks. This can enable them to do the monitoring while performing the other tasks, such as weeding, pest-control, spraying, etc., thereby saving time and money.Agrofocal succesfully developed the system during Phase I for farm operations. During Phase II, the team will conduct field trials to assess the systems ability to determine crop yield and accuracy while monitoring operational robustness. In addition, the team will evaluate and refine the AI models based on the field trial data. Succesful completion of the Phase II objectives will set the stage from product launch and commercialization.Agrofocal's crop monitoring solution will provide useful insights in real-time, will be easy to use, and will fit seamlessly with existing farming operations. It will enable farmers to make informed decisions based on objective crop measurements. The insights provided will drive efficiencies in agriculture that will increase production, reduce costs, and lessen environmental impact. These efficiencies will positively change agriculture to the benefit of everyone.
Animal Health Component
20%
Research Effort Categories
Basic
5%
Applied
20%
Developmental
75%
Goals / Objectives
The overall goal for Phase II is to run extensive trials on the system developed in Phase I and solve next set of problems to get the product ready for commercialization and real-life deployment.Objective 1: Field trials for yield: Establish accuracy of yield forecast via extensive full-system trials in farms. Run extensive field trials with our system to predict yield in farms and establish the accuracy of the numbers by comparing them to the actual yield numbers obtained from the farm harvest. In case of discrepancies, find the root-cause, adjust the system and the AI model accordingly, and retry.Objective 2: Field trials for crop health: Establish accuracy of crop health issue detection via extensive full-system trials in farms. Run extensive field trials with our system to detect plant health issues and establish the accuracy of detection by confirming with in-person check that the issues exist at the GPS location where the system predicted they did. In case of discrepancies, find the root-cause, adjust the system and the AI model accordingly, and retry.Objective 3: Field trials for robustness: Establish system robustness via extensive full-system trials in farms. Run extensive field trials to subject our system to various stress scenarios and establish that the system either continues to work seamlessly through these scenarios or handles them in a manner that will feel logical to the user and allow them to take corrective action.Objective 4: AI: Continue to refine AI models to make them more accurate. Continue to refine and improve the AI models based on the learning from the field trials with the goal to achieve the overall system accuracy metrics as specified in Objective 1 and 2.
Project Methods
Objective 1: Field trials for yield: Establish accuracy of yield forecast via extensive full-system trials in farms. Methods: For strawberry, the AI and Computer Vision models running on the compute box will detect and count number of berries at different stages of development, from a flower to a ripe berry, in the images received from the camera. Given the knowledge provided by our agriculture collaborators about the typical time it takes per season for a berry to progress through each stage of development and become a ripe berry, the software will convert the counts of berries in different stages of development into a weekly yield forecast that goes out 5 weeks. Strawberry is a crop that gets harvested every week throughout the season. We will get the number of berries harvested per week from the fields where we run the trial and compare them to our weekly forecast. If an extreme weather event, like heavy rains or frost, happens that can significantly impact the actual counts from the field during the 5 weeks, then we'll discard this trial and begin a new one. (As a side note, the forecast count can be used to estimate how much fruit was lost for insurance purposes in case of such weather events).For almonds, the AI and Computer Vision models running on the compute box will detect and count number of nuts in the images of trees received from the camera. During Phase 1 work, we have developed a numerical method to account for occluded nuts on the trees. Using this method, we will estimate the total number of nuts on trees based on number of nuts seen by the camera. Unlike strawberry, almonds are harvested once in a season and all at the same time. Post-harvest, farmers already track their yield in terms of pounds of nuts per acre of their farms. In addition, they also estimate the average weight of almond kernel for their harvest. This information is important for them to price their almonds. We will use the pounds of nuts per acre and the average weight of the kernels from that field where we run our trial to estimate the number of nuts harvested from that field. We will then compare this number to our forecasted number of nuts.For grapes, the farmers care about two things when it comes to yield: the number of bunches and how many of them are ripe. The AI and Computer Vision models running on the compute box will detect and count number of bunches in the images of vines received from the camera. In addition, the compute box will run a ripeness detection model that looks for color on the grapes to classify bunches as ripe or not ripe. This model was developed during Phase 1 work. Like almonds, grapes are also harvested once in a season. For table grapes, the number of bunches harvested is tracked by the farmers as they handpick and pack in boxes in the field. For wine grapes, the harvest is done by machine and tons of grapes harvested per acre is tracked. Before harvest, the farmers estimate the average weight per bunch by sampling. This is important for them to estimate the sugar content of the grapes. We'll either use the direct bunch count in case of table grapes or use tons per acre and bunch weight information to estimate the bunch count for wine grapes. We'll compare this number to our forecast number of bunches times the percentage ripe.Objective 2: Field trials for crop health: Establish accuracy of crop health issue detection via extensive full-system trials in farms. As part of this objective, our goal is to establish the accuracy of crop health issue detection by our system by confirming through in-person check that the issue exists at the GPS location where the system predicted it did. What health issue to monitor for each crop was decided based on the feedback from our collaborating customers.Methods: For strawberry, we track three health issues: (i) Damaged berry, (ii) Chlorosis on leaves, (ii) Plant die-off. A berry can get deformed and damaged due to pest, like lygus, infestation or due to weather. In either case it is not harvestable. Knowing where these damaged berries are located will help farmers check for pest issues in that area and take quick action. Chlorosis is a condition of leaves losing chlorophyll. This can occur due to many reasons, such as mite infestation, nutrition deficiencies, water stress, or just old leave. Knowing where excessive amounts of Chlorosis is located will help farmers check for mite, nutrition, or water issue in that region. Plant die-off occurs when a young plant does not take root and dies. There may be several reasons for this, such as it did not get planted right, or nursery delivered unhealthy plants. Knowing where excessive amounts of plant die-offs are located will help farmers take appropriate actions.For grapes, we track two health issues: (i) Decay on bunches, and (ii) Chlorosis on leaves. Decayed grapes can be symptomatic of pest issues, like powdery mildew or botrytis. Knowing where they exit, especially before they spread widely, can help farmer take corrective actions early. Chlorosis on leaves for grapes can point to similar issues like they do strawberry. Knowing their location can help farmers take appropriate actions.Objective 3: Field trials for robustness: Establish system robustness via extensive full-system trials in farms. In this objective, our goal is to establish through extensive field test that the system is robust to real life operations in farms and can consistently operate for an entire day in rugged conditions. This is important before we can sell and deploy the system widely.Methods: As part of this objective, we'll stress test the system. We'll run in at different times of day to test with different light and shadow directions. To get different angles at which the sunlight, and therefore the shadow, falls on the plants, we'll test the system in following three segments of the day: 8am-11am, 11am-2pm, and 2pm-5pm. We'll also attempt to pick a mix of overcast and sunny days for testing. We'll test the system to run continuously in farms for up to 5 hours. This will ensure that we can at least cover the entire morning hours, from 7 to noon, during which majority of farming activity happens. We'll vary the vehicle speeds between 2mph to 4mph, which is the typical speed range between which the farm vehicles operate. We'll introduce random slowdown, stops and reversing of vehicle. We'll subject camera and compute box to regular vibrations as part of regular driving and exaggerated vibrations by revving the engine. We'll disconnect camera intermittently and permanently. We'll remove power from compute box to simulated power cable coming loose. We'll disconnect Agrofocal app from compute box.Objective 4: AI: Continue to refine AI models to make them more accurate. AI models need to be continually trained with new images and refined to ensure improved accuracy over time.Methods: The work for this objective will be dictated by learning from the trials done under Objective 1 and 2. These objectives will require us to retrain models for new scenarios encountered during real-life testing. As we go through the trials, we'll identify issue that is causing lower accuracy. In some cases, we may need to collect new set of images so that we can train the model for a scene that was never encountered before. In other cases, we may need to update the pre-processing step (e.g., tune motion tracking, handle glare in an image) or update the post-processing step (e.g., tune result filtering) to improve accuracy. The work on this objective will continue until we meet the success criteria outlined for Objectives 1 and 2. If time permits, we'll begin training AI models for other crop types, such as Citrus and Walnuts, to prepare us for expanding beyond the current three crop types, as we exit Phase II.