Periodic Reporting for period 1 - EchoYieldEstimation (A Sonar Sensing System for Crop Yield Estimation)
Periodo di rendicontazione: 2022-10-01 al 2024-03-31
With the growing world population, accelerated climate changes and global migration, nutritional security becoming one of the major challenges. This situation calls for fast development and utilisation of agricultural technology that will push agricultural yield production to its full potential.
Yield estimation in agricultural production is therefore very important. It is central for both agricultural planning as well as for successful agroeconomic management. It is currently almost solely done manually without assisting technology and it is therefore inaccurate and partial. To meet this challenge, we offer a unique sonar sensing system for estimating vegetable yield. Our technology, as demonstrated in a patent and in a recent peer reviewed paper, has unexampled advantages compared to other sensors. The sonar-based crop estimating sensor has been patented and initial tests show that it is capable of estimating pepper yields with 90% accuracy. In this project, we intend to expand those tests to other vegetables including tomatoes and cucumbers, aiming to provide farmers with a reliable tool for crop estimation. The project will provide the necessary steps for establishing a spin-off company that will exploit this invention and provide a product to the benefit of European and worldwide farmers. Our goal is to collect data from end-users in the EU, to improve the Artificial Intelligence algorithm that analyses the measurements of the sensor and to be ready at the end of the project for raising further investments to develop the full product. Our team is composed of sonar, robotics and business experts. We will collaborate with agri-robotics companies and agricultural stake-holders in Europe to fulfil the goals of the project.
Yield estimation in agricultural production is therefore very important. It is central for both agricultural planning as well as for successful agroeconomic management. It is currently almost solely done manually without assisting technology and it is therefore inaccurate and partial. To meet this challenge, we offer a unique sonar sensing system for estimating vegetable yield. Our technology, as demonstrated in a patent and in a recent peer reviewed paper, has unexampled advantages compared to other sensors. The sonar-based crop estimating sensor has been patented and initial tests show that it is capable of estimating pepper yields with 90% accuracy. In this project, we intend to expand those tests to other vegetables including tomatoes and cucumbers, aiming to provide farmers with a reliable tool for crop estimation. The project will provide the necessary steps for establishing a spin-off company that will exploit this invention and provide a product to the benefit of European and worldwide farmers. Our goal is to collect data from end-users in the EU, to improve the Artificial Intelligence algorithm that analyses the measurements of the sensor and to be ready at the end of the project for raising further investments to develop the full product. Our team is composed of sonar, robotics and business experts. We will collaborate with agri-robotics companies and agricultural stake-holders in Europe to fulfil the goals of the project.
Since the commence of the project we commenced several activities according to the proposal’s workplan.
1. Data collection – In order to train the CNN (Convolutive Neural Network) one has to collect data from the field. We conducted a vast experiment in a cherry tomato growing greenhouse in Pdeya Israel. We were able to collect and label more then 3200 sonar scans (See Fig. 1).
2. Data processing - We used the data to train a Resnet 18 CNN. We achieved good results in the detection of tomatoes (90% detection) how ever the quantification of the amount of tomatoes was less accurate (about twice better than a random pick). 60% detection in 3 categories (1-7 tomatoes, 8-12 tomatoes, 13-37 tomatoes) and 38% detection in 5 categories (0 tomatoes, 1-5 tomatoes, 6-9 tomatoes, 10-13 tomatoes, 14-37 tomatoes).
3. Prototype development – We made an agreement with a swiss company HighDim GmbH for the development of a sonar sensing system prototype. The sensing system is based on a hybrid FPGA design that has custom modules for signal processing and the ability to use a CNN. The device will collect and process the data and transmit the results via Wi-Fi. HighDim finished designing the device and currently working on the hardware, software and firmware (See Fig. 2).
4. Business development – We designed a pitch-deck for the project and contacted several Business development agencies in Basel area for aiding in the development of Startup companies (Basel Area Swiss, Basel Land Development Support).
5. Collaboration with agricultural experts - We contacted Pepper growers in Italy and will conduct there the next phase of experiments. We prepared a questioner for data collection from farmers in order to estimate the potential market of the future product.
1. Data collection – In order to train the CNN (Convolutive Neural Network) one has to collect data from the field. We conducted a vast experiment in a cherry tomato growing greenhouse in Pdeya Israel. We were able to collect and label more then 3200 sonar scans (See Fig. 1).
2. Data processing - We used the data to train a Resnet 18 CNN. We achieved good results in the detection of tomatoes (90% detection) how ever the quantification of the amount of tomatoes was less accurate (about twice better than a random pick). 60% detection in 3 categories (1-7 tomatoes, 8-12 tomatoes, 13-37 tomatoes) and 38% detection in 5 categories (0 tomatoes, 1-5 tomatoes, 6-9 tomatoes, 10-13 tomatoes, 14-37 tomatoes).
3. Prototype development – We made an agreement with a swiss company HighDim GmbH for the development of a sonar sensing system prototype. The sensing system is based on a hybrid FPGA design that has custom modules for signal processing and the ability to use a CNN. The device will collect and process the data and transmit the results via Wi-Fi. HighDim finished designing the device and currently working on the hardware, software and firmware (See Fig. 2).
4. Business development – We designed a pitch-deck for the project and contacted several Business development agencies in Basel area for aiding in the development of Startup companies (Basel Area Swiss, Basel Land Development Support).
5. Collaboration with agricultural experts - We contacted Pepper growers in Italy and will conduct there the next phase of experiments. We prepared a questioner for data collection from farmers in order to estimate the potential market of the future product.
The current results, as described in the former sub-section, are progressing according to the planned PoC. The development of a product prototype goes beyond the current stage of the research plan. We see many benefits in developing a standalone system that can perform local processing of the sonar data.
In order to fully develop this PoC into a Startup we will need further resources and we intend to apply to EU supporting schemes such as Eurostars in order to obtain them.
In addition to crop estimation, we identified additional application to our sonar technology. The system can be used as a complementary obstacle detection and classification for UAV/UGVs. We intend to submit a PoC project that will exploit the current system in these directions.
Our current technology is patented for agricultural applications. We intend to examine whether this IP can be extended to the new directions we discovered during the current PoC project.
In order to fully develop this PoC into a Startup we will need further resources and we intend to apply to EU supporting schemes such as Eurostars in order to obtain them.
In addition to crop estimation, we identified additional application to our sonar technology. The system can be used as a complementary obstacle detection and classification for UAV/UGVs. We intend to submit a PoC project that will exploit the current system in these directions.
Our current technology is patented for agricultural applications. We intend to examine whether this IP can be extended to the new directions we discovered during the current PoC project.