About C-Crop Technology

C-Crop technology revolutionizes vineyard management by empowering farmers with precise monitoring capabilities. Our innovative platform offers detailed insights into vineyard yield and quality through precise measurements, accessible anytime and anywhere from smartphone devices

C-Crop technology

C-Crop's unique approach harnesses the sensors and lenses of standard smartphones to capture videos, creating a near 3D representation without the need for a dedicated 3D camera. By utilizing the video feed, the technology generates depth information, resulting in a pseudo-3D effect. This information is then processed using cloud-based deep learning tools to provide precise insights into vineyard yield, including clusters density and size, as well as berry number and size, starting from the early developmental stages.

Few simple steps to measure your vineyards yieldי

1. Open the C-Crop app on your mobile device and access the dedicated screen for marking your plots.

2. Utilize the app's video recording feature to capture short videos of your vineyard. Ensure that you evenly spread the samples throughout the vineyard.

3. Once you have an internet connection, all videos will be automatically uploaded to C-Crop cloud.

4. After the processing is complete, which occurs rapidly and depends on your internet connection, you will receive a user-friendly report directly on the C-Crop app.

C-Crop Value for You

1. Precise monitoring of your vineyard's yield throughout the entire season, enabling proactive treatments and optimization.

2. Sub-Plot Level Yield Data: Access detailed yield and yield components data at the sub-plot level, empowering you to make informed and right decisions.

3. Identify Performance Gaps: Identify and address any performance gaps in your vines to ensure optimal growth and productivity.

4. Efficient Resource Management: Effectively manage your resources, manpower, and inputs by leveraging C-Crop's comprehensive data.

Use Case: ​Accurate Table Grape Yield Estimation

Objective: To accurately estimate the yield of a table grape vineyard plot spanning 4.0 hectares in South Africa.

Data Collection:

* 320 video samples were taken in the vineyard, with an average of 80 videos per hectare.

* These video samples were analyzed to estimate overall yield, as well as yield components including clusters density, clusters size, berries number and size per cluster.

Analysis and Yield Estimation:

* The video samples were analyzed using C-Crop’s platform to estimate the overall yield of the vineyard.

* Yield components, including clusters density, clusters size, and berries number and size per cluster, were also analyzed.

* The estimated yield was then compared to the actual yield, which was determined by weighing the harvested fruit in the packing house.

Outcome:

* The C-Crop platform achieved a high accuracy rate of more than 92% in predicting the yield of the vineyard.

* In addition to yield estimation, the platform identified the variability of yield within the vineyard plot.

Through statistical analysis, two distinct sub-plots were identified, exhibiting a 60% difference in yield between the highest (blue) and lowest (red) sub-plots.

* Further investigation conducted by the grower team revealed that the difference in yield was attributed to the varying operation of two irrigation systems.

Further statistical

Further statistical clustering analysis identified that the highest sub plot (blue) had 36% more clusters per area, 20% more berries and the berries are 14% heavier compared to the lowest sub plot (blue). The technology estimate yield with a high accuracy rate of 94%. The technology identified that a significant portion of yield loss, 36% is due to over thinning. The difference in yield between the sub plots in the vineyard has been found to be related to two separate irrigation systems that are operated in this vineyard.