About C-Crop Technology

C-Crop technology platform revolutionizes fruit crop management across the value chain by providing stakeholders an accurate data accessible anytime and anywhere from smartphone devices

C-Crop technology

Our advanced technology platform analyses video samples and their corresponding smartphone sensors data. Sophisticated algorithms fuse various data layers, calculated by dedicated deep learning networks, to enhance the accuracy and precision of yield measurements. The system ensures accurate and precise measurements throughout the growing season, even under diverse environmental conditions, using standard smartphone models without additional hardware.

Few simple steps to measure your crop 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 plot. Ensure that you evenly spread the samples throughout the plot.

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 to your C-Crop application and desktop

C-Crop Value for You

1. Effective Crop Management: Precise data enables effective management of crop yield throughout the entire season, allowing proactive treatments and optimization.

2. Performance Optimization: Identify and address performance gaps in plots to ensure optimal growth and productivity

3. Yield Forecasting and Quality Assessment: Determine yield forecasts and quality from early growing stages. Sharing accurate, standardized yield data among stakeholders improves efficiency and reduces costs across the entire market channel.

4. Innovation Enhancement: Accurate, standardized yield data facilitate the development and implementation of new treatments, varieties, and practices.

A Case Study: Accurate Table Grape Yield Measurement

Objective:

To accurately measure the yield performance of a 4-hectare table grape vineyard plot in South Africa.

Data Collection:

* Approximately 300 video samples were taken in the vineyard, about 50 days before harvest, following C-Crop’s manual guide.

* This data was automatically uploaded to the C-Crop secure analysis platform

Data Analysis:

* The video samples were analyzed using C-Crop’s platform, showing how the yield and its characteristics (cluster density and size, and berry number and size) are distributed within the plot.

* The calculated yield was then compared to the actual harvested yield

Outcome:

* The C-Crop platform achieved more than 90% accuracy in calculating yield and yield characteristics

* C-Crop platform identified high variability in yield distribution within the plot, revealing two distinct sub-plots with a 40% difference in yield between them (highest in blue vs. lowest in red).

* Further investigation by the agrotechnical team found that this difference was due to an improper operation of one of the irrigation controllers that were responsible for regular irrigation of the plot

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.