Description:“RootLens- A Smart Root Imaging System”
Description
Field of Invention:
The device is a root measurement smart system that is designed and fabricated by integrating the concepts of computational, electronics, and plant sciences. The automated prototype was developed to capture in situ root images i.e. grown under soil. These root images were analyzed through integrated software named Ro-SOFT to calculate root parameters i.e. root length, area, diameters, and others which are the essential parameters for supervising the growth of crops.
Background of Invention
Roots serve many essential functions such as uptake of water and nutrients for plant growth, act as storage organs, anchor the plants to the soil, and are the site of interactions with biotic organisms in the root zone. The dynamics of root growth and development in response to changing water and nutrient grades of the soil provide a black box for exploring natural variation to identify important root traits to improve plant productivity in agricultural systems (Lynch, 1995; Kano, 2011). Methods of root system architecture (RSA) trait analysis mostly include excavation or washed soil core, which destroy the topology of RSA and degrade the lateral root number. In recent years, advanced image-based root phenotyping posed a revolution (scanners or cameras) for computing the morphometric traits of root and shoot (Adu et al., 2014; Le Marié et al., 2014). Furthermore, non-invasive methods of root imaging can be done with X-ray computed tomography (X-ray-CT), magnetic resonance imaging (MRI), or neutron tomography (Leitner et al., 2014; Metzner et al., 2015). Another revolutionized field-based method applied in RSA studies is minirhizotron (MR) which allows direct observations of plant roots falling in the rhizosphere zone. Besides phenotyping, in situ root studies are increasingly important to understand the factors controlling agricultural yields in diverse environmental conditions. MRs have emerged as a sound tool for understanding the root responses of crop systems. However, the major lacuna in MR application is the higher cost and less throughput images.
The phenotyping of plant roots is a challenging task and poses a major lacuna in plant root research. The roots rhizosphere zone is affected by several environmental cues among which salinity, drought, heavy metal, and soil pH are key players. Among biological factors, fungal, nematode, and bacterial interactions with roots are vital for improving nutrient uptake efficiency in plants. The subterranean nature of a plant root and the limited number of approaches for root phenotyping offer a great challenge to plant breeders to select a desirable root trait under different stress conditions. Identifying key root traits can provide a basic understanding of generating crop plants with an enhanced ability to withstand various biotic or abiotic stresses. For instance, crops with improved soil exploration potential, phosphate uptake efficiency, water use efficiency, and others. The present study was conducted to explore the different field and laboratory-based methods for root system studies viz. hydroponics, rhizotron, rhizoslide, minirhizotrons, X-ray computed tomography (X-ray-CT), and magnetic resonance imaging (MRI). The present work highlights the advantages and discrepancies of available systems developed for plant root phenotyping. For instance, hydroponics does not permit precise and practical relevant root quantification, despite 2D-rhizotron being particularly suitable for root trait quantification, root-shoot physiological relations, and root system responses to local soil conditions. The rhizoslide method allows studying the root growth of crown roots and seminal roots independently under heterogeneous environmental conditions. Further 3D imaging by X-ray CT and MRI techniques provides the opportunity for quantifying RSA traits. Non-invasive study of RSA (X-ray CT, MRI, Minirhizotron) is one of the greatest challenges in tracing the important contributing root traits under different environmental stimuli. The available non-invasive methods are highly expensive and have less practical relevance. A literature survey showed the absence of a reliable and economical Indian-made automated root image analysis system for both ex-situ and in-situ analysis. In this context, the present study developed an automated prototype for plant root imaging and analysis. This setup allows a user to perform repeatable non-destructive observations of root images and thus help in studying in-situ root development and ex-situ root image analysis.
Summary of Invention:
An objective of the present invention is to fabricate a smart system for monitoring the growth of roots for growing supercrops.
Root trait identification plays a pivotal role in crop improvement programs. In-depth analysis of RSA is an urgent need to use the latest molecular technologies, to collaborate the knowledge of root studies and breeding programs.
RSA traits viz. water use efficiency and nitrogen use efficiency are highly desired for the generation of supercrops.
Aerial and below-ground plant phenotyping in combination allows the selection of specific genotypes and practices on farms to enhance productivity gain. However, this requires a paradigm shift in developing new approaches, timelines, and intensity of research work programs related to RSA phenotyping.
3D imaging by X-ray CT and MRI techniques provides the most applicable and practical relevance for quantifying RSA traits. Non-invasive study of RSA (X-ray CT, MRI, Minirhizotron) is one of the greatest challenges in tracing the important contributing root traits under different environmental stimuli.
The advanced automated prototype is designed to examine and measure key root system architecture details like the root number, root length, root diameter, and root surface area of plant roots collected from the field.
RootLens: A complete automated and portable equipment designed for both in-situ and ex-situ root system architecture (RSA) analysis.
The prototype is portable and presents an onspot analysis of plant roots in the field combining the features of Ro-CAM and Ro-SOFT.
The prototype allows access to the researchers and farmers to quickly assess the parameters and environmental conditions stressing the plant roots.
The smart system developed is economical for the use of the masses.
Detailed description of Invention:
The present invention provides a smart system for capturing the images and processing them for analyzing the parameters for root growth.
Material and Methods
Ro-POT: The Ro-POT is molded from acrylic glass and comprises an external pot measuring 35x25cm, which encapsulates an internal pot of dimensions 35x20cm. The intervening 5cm space is filled with either soil or coco peat. Notably, this interspace is subdivided into three equidistant compartments, affording distinct environmental conditions within the Roo-POT.
Ro-CAM. The portable system outlined herein comprises a configuration for acquiring high-resolution images of root systems cultivated within Ro-POT, attaining depths of up to 25 cm. Using infrared-based imaging systems housed within Ro-CAM, the cameras are interconnected with a HAT array to ensure synchronized functionality. Subsequently, the HAT array is interfaced via a common strip to a Raspberry Pi4 (CPU). The process involves the systematic capture of root images at varying depths, facilitating the generation of composite images, which are subsequently archived on the SD card embedded in the Raspberry Pi4. For capturing the root images: three in one, combined three cameras into one frame, each camera with a maximum resolution of 2028(H)x1520(V). Wide compatibility: Compatible with the latest Raspberry Pi camera software (libcamera) and uses the official Pi camera tuning algorithm. It also supports multi-platform multi-camera solutions such as Raspberry Pi, Jetson Nano, Xavier NX, and many more. This Camarray HAT is also compatible with Raspberry Pi3 and Raspberry Pi (V1/V2/HQ) cameras.
Key features of the Raspberry Pi 4:
Broadcom BCM2711 quad-core Cortex-A72 (ARMv8) 64-bit SoC. Options for 2GB, 4GB, or 8GB LPDDR4 RAM, provide improved performance compared to earlier models. Dual-band 802.11b/g/n/ac wireless LAN, allowing for faster and more reliable Wi-Fi connections. Bluetooth 5.0, provides enhanced wireless communication capabilities. Gigabit Ethernet for high-speed wired networking. Two USB 3.0 ports for faster data transfer. Two USB 2.0 ports for connecting peripherals. Dual micro-HDMI ports that support up to 4K video output. H.265 (4Kp60 decode), H.264 (1080p60 decode, 1080p30 encode) video capabilities. Improved multimedia performance compared to previous models. 40 GPIO pins, maintaining compatibility with previous Raspberry Pi models. Improved pinout for easier and more flexible hardware projects. USB Type-C connector for power supply, replacing the micro-USB connector used in previous models. The Raspberry Pi4 can generate more heat due to its increased performance, so it includes a heat sink to help with cooling. MicroSD card slot for storage, as in previous models.
In-situ image acquisition protocol: The steps followed for the acquisition of plant root images are:
I. Roo-CAM (RoCAM) Integration: Implant RoCAM seamlessly within Roo-POT for enhanced functionality and compatibility.
II. Code Optimization: Streamline and optimize the existing codebase to improve overall system performance and responsiveness.
III. Light Source Provision: Incorporate a dedicated light source to ensure optimal visibility for cameras, facilitating clear image capture.
IV. Camera Detection and Command Execution: Develop a robust system for camera detection and execution of commands. Example: Run command, capture images with HAT ARAY on RASSPI4, display captured images, and store the images for further processing.
V. Camera C2 Operation: Implement specific operations for Camera C2: Capture images with HAT ARAY on RASSPI4, display captured images, and store the images for subsequent analysis.
VI. Camera C3 Operation: Replicate similar operations for Camera C3, ensuring consistent image capture, display, and storage functionalities.
VII. Image Transfer for Analysis: Establish a reliable mechanism for transferring stored images to a laptop: Option 1: Utilize WiFi for seamless data transfer and Option 2: Transfer images via a pen drive for offline analysis.
VIII. Analysis and Interpretation: Provide tools or software on the laptop for comprehensive image analysis, allowing for interpretation and extraction of relevant information.
3. Ro-SOFT: Ro-SOFT stands as a software tool precisely designed to operate on the root images captured by Ro-CAM, playing a pivotal role in the extraction of crucial root traits. This includes root length, root number, root diameter, and other related characteristics essential for a comprehensive analysis of the root system. The software is divided into two sections: Root Parameters Calculation and Graphical User Interface (GUI) This part of the software is used to calculate the parameters of the uploaded image of the roots.
Root Parameters Calculation:
I. Image tool: Accepts the merged image, taken from 3 different cameras after being merged into a single image in .png, .jpg, or .jpeg format for further analysis

II. Background remover tool: Distinguish between roots and the soil. Isolate roots from the soil and remove the background soil from the uploaded image.

III. Contour identification tool: Identifies the contours to monitor and store continuity and outline boundaries of different roots.

IV. Parameter calculation tool: Calculates the length, diameter, and area of different roots based on the number of pixels returned through the contour identification tool.

V. Colour coding: Sorts the roots based on their length in decreasing order and colour codes them for ease of classification. The longest two roots are colored red. The next three roots are coloured blue and the remaining roots are coloured green.
Graphical User Interface:
The GUI is a simple application to upload an image, remove its background, and calculate root properties like length, area, and diameter. It consists of the following components:
I. Upload image button: Uploads the merged image using a file dialog. Images in formats like .png, .jpg, and .jpeg are accepted.
II. Remove background button: Initiates the background remover tool to remove the background soil from the uploaded image to achieve precise root measurement.
III. Number of roots dropdown: Selects the number of roots for parameter calculation. For now, the number ranges from 1 to 20.
IV. Calculate parameters button: initiates the contour identification tool followed by the parameter calculation tool to calculate parameters (length, area, diameter) of roots based on the number selected in a dropdown.
V. Length, Area, and Diameter labels: Displays the calculated parameters respectively Other Features of GUI:
I. Root images 1, 2, 3: The GUI displays the original images and images with removed background and processed images.
II. Colour code: The roots are color-coded based on length for easy distinction. The longest two roots are colored red. The next three roots are colored blue and the remaining roots are colored green.
III. Image preview: Double-clicking on root image 1, 2, or 3 opens a new tab with a larger preview of the image.
Automated Root Imaging platform:
The root imaging platform is comprised of a rotating plate shown in the top view where the Ro-POT with a plant can be placed (A, B), the plate rotation is facilitated through a servo motor as shown in the interior view (C). The control system controls the rotation and capturing of root images using Roo-CAM and is displayed on the screen (D). This system can capture plant images from different angles.

, Claims:RootLens- A Smart Root Imaging System”
Claims
A RootLens smart system comprising:
Ro-POT, Ro-CAM, and integrated software Ro-SOFT and Automated root imaging platform.
Claim 1: The Ro-POT is molded from acrylic glass and comprises an external pot measuring 35x25cm, which encapsulates an internal pot of dimensions 35x20cm. The intervening 5 cm space is filled with either soil or coco peat. Interspace is subdivided into four equidistant compartments, affording distinct environmental conditions within.
Claim 2: Ro-CAM, a portable system outlined herein captures high-resolution images of root systems cultivated within Ro-POT at a depth of up to 25 cm. Using infrared-based imaging systems housed within Ro-CAM, the cameras are interconnected with a HAT array to ensure synchronized functionality. Subsequently, the HAT array is interfaced via a common strip to a Raspberry Pi 4 (CPU). The process involves the systematic capture of root images at varying depths, facilitating the generation of composite images, which are subsequently archived on the SD card embedded in the Raspberry Pi4.
Three cameras of resolution 2028(H)x1520(V) are incorporated in Ro-CAM to capture precise images of the root system. The cameras accessed through the CamHAT array module are compatible with the latest Raspberry Pi4 processor.
Claim 3: Ro-SOFT is a software tool precisely designed to operate on the root images captured. It plays a pivotal role in the extraction of crucial root traits like root length, root number, root diameter, and other related characteristics essential for a comprehensive analysis of the root system.
Claim 4: An automated root imaging platform that provides controlled to-and-fro movement of the platform on which Ro-POT can be placed.
Description:
1. The Ro-POT as claimed in claim 1, provides space filled with soil or coco-peat wherein plants are grown under preferable natural conditions.
2. The Ro-POT space as claimed in claim 1 is divided into four compartments wherein plants can be grown under different conditions for analyzing the root behavior.
3. The Ro-CAM as claimed in claim 2, is an imaging device inserted in the Ro-POT to project/capture live images of the root system into the memory pod after desired regular intervals.
4. The Ro-CAM as claimed in claim 2 is portable and presents an on-the-spot analysis of plant roots in the field.
5. The Ro-SOFT as claimed in claim 3 is developed to process root images to extract root phenotyping traits from the images captured using Ro-CAM.
6. The Ro-SOFT as claimed in claim 3 also extracts the root phenotyping traits of the washed root images captured using mobile phones or any cameras.
7. The Automated root imaging platform as claimed in claim 4 serves as an automated platform for taking the images of the plant growing inside a Ro-POT at regular intervals.