PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments

Automated Imaging to Evaluate the Exogenous Gibberellin (Ga3) Impact on Seedlings from Salt-Stressed Lettuce Seeds

Salinity stress is a common challenge in plant growth, impacting seed quality, germination, and general plant health. Sodium chloride (NaCl) ions disrupt membranes, causing ion leakage and reducing seed viability. Gibberellic acid (GA3) treatments have been found to promote germination and mitigate salinity stress on germination and plant growth. 'Bauer' and 'Muir' lettuce (Lactuca sativa) seeds were soaked in distilled water (control), 100 mM NaCl, 100 mM NaCl + 50 mg/L GA3, and 100 mM NaCl + 150 mg/L GA3 in Petri dishes and kept in a dark growth chamber at 25 °C for 24 h. After germination, seedlings were monitored using embedded cameras, capturing red, green, and blue (RGB) images from seeding to final harvest. Despite consistent germination rates, 'Bauer' seeds treated with NaCl showed reduced germination. Surprisingly, the 'Muir' cultivar's final dry weight differed across treatments, with the NaCl and high GA3 concentration combination yielding the poorest results (p < 0.05). This study highlights the efficacy of GA3 applications in improving germination rates. However, at elevated concentrations, it induced excessive hypocotyl elongation and pale seedlings, posing challenges for two-dimensional imaging. Nonetheless, a sigmoidal regression model using projected canopy size accurately predicted dry weight across growth stages and cultivars, emphasizing its reliability despite treatment variations (R2 = 0.96, RMSE = 0.11, p < 0.001).

Low-Cost Imaging to Quantify Germination Rate and Seedling Vigor across Lettuce Cultivars

The survival and growth of young plants hinge on various factors, such as seed quality and environmental conditions. Assessing seedling potential/vigor for a robust crop yield is crucial but often resource-intensive. This study explores cost-effective imaging techniques for rapid evaluation of seedling vigor, offering a practical solution to a common problem in agricultural research. In the first phase, nine lettuce (Lactuca sativa) cultivars were sown in trays and monitored using chlorophyll fluorescence imaging thrice weekly for two weeks. The second phase involved integrating embedded computers equipped with cameras for phenotyping. These systems captured and analyzed images four times daily, covering the entire growth cycle from seeding to harvest for four specific cultivars. All resulting data were promptly uploaded to the cloud, allowing for remote access and providing real-time information on plant performance. Results consistently showed the 'Muir' cultivar to have a larger canopy size and better germination, though 'Sparx' and 'Crispino' surpassed it in final dry weight. A non-linear model accurately predicted lettuce plant weight using seedling canopy size in the first study. The second study improved prediction accuracy with a sigmoidal growth curve from multiple harvests (R2 = 0.88, RMSE = 0.27, p < 0.001). Utilizing embedded computers in controlled environments offers efficient plant monitoring, provided there is a uniform canopy structure and minimal plant overlap.

Wearable sensor supports in-situ and continuous monitoring of plant health in precision agriculture era

Plant health is intricately linked to crop quality, food security and agricultural productivity. Obtaining accurate plant health information is of paramount importance in the realm of precision agriculture. Wearable sensors offer an exceptional avenue for investigating plant health status and fundamental plant science, as they enable real-time and continuous in-situ monitoring of physiological biomarkers. However, a comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive. In this study, we provide a meticulous description and systematic synthesis of recent research progress in wearable sensor properties, technology and their application in monitoring plant health information. This work endeavours to serve as a guiding resource for the utilization of wearable plant sensors, empowering the advancement of plant health within the precision agriculture paradigm.

A method of cotton root segmentation based on edge devices

The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root.

Development of an accurate low cost NDVI imaging system for assessing plant health

BACKGROUND

Spectral imaging is a key method for high throughput phenotyping that can be related to a large variety of biological parameters. The Normalised Difference Vegetation Index (NDVI), uses specific wavelengths to compare crop health and performance. Increasing the accessibility of spectral imaging systems through the development of small, low cost, and easy to use platforms will generalise its use for precision agriculture. We describe a method for using a dual camera system connected to a Raspberry Pi to produce NDVI imagery, referred to as NDVIpi. Spectral reference targets were used to calibrate images into values of reflectance, that are then used to calculated NDVI with improved accuracy compared with systems that use single references/standards.

RESULTS

NDVIpi imagery showed strong performance against standard spectrometry, as an accurate measurement of leaf NDVI. The NDVIpi was also compared to a relatively more expensive commercial camera (Micasense RedEdge), with both cameras having a comparable performance in measuring NDVI. There were differences between the NDVI values of the NDVIpi and the RedEdge, which could be attributed to the measurement of different wavelengths for use in the NDVI calculation by each camera. Subsequently, the wavelengths used by the NDVIpi show greater sensitivity to changes in chlorophyll content than the RedEdge.

CONCLUSION

We present a methodology for a Raspberry Pi based NDVI imaging system that utilizes low cost, off-the-shelf components, and a robust multi-reference calibration protocols that provides accurate NDVI measurements. When compared with a commercial system, comparable NDVI values were obtained, despite the fact that our system was a fraction of the cost. Our results also highlight the importance of the choice of red wavelengths in the calculation of NDVI, which resulted in differences in sensitivity between camera systems.

A low-cost and open-source solution to automate imaging and analysis of cyst nematode infection assays for Arabidopsis thaliana

BACKGROUND

Cyst nematodes are one of the major groups of plant-parasitic nematode, responsible for considerable crop losses worldwide. Improving genetic resources, and therefore resistant cultivars, is an ongoing focus of many pest management strategies. One of the major bottlenecks in identifying the plant genes that impact the infection, and thus the yield, is phenotyping. The current available screening method is slow, has unidimensional quantification of infection limiting the range of scorable parameters, and does not account for phenotypic variation of the host. The ever-evolving field of computer vision may be the solution for both the above-mentioned issues. To utilise these tools, a specialised imaging platform is required to take consistent images of nematode infection in quick succession.

RESULTS

Here, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method based on a pre-existing nematode infection screening method in axenic culture. A cost-effective, easy-to-build and -use, 3D-printed imaging device was developed to acquire images of the root system of Arabidopsis thaliana infected with the cyst nematode Heterodera schachtii, replacing costly microscopy equipment. Coupling the output of this device to simple analysis scripts allowed the measurement of some key traits such as nematode number and size from collected images, in a semi-automated manner. Additionally, we used this combined solution to quantify an additional trait, root area before infection, and showed both the confounding relationship of this trait on nematode infection and a method to account for it.

CONCLUSION

Taken together, this manuscript provides a low-cost and open-source method for nematode phenotyping that includes the biologically relevant nematode size as a scorable parameter, and a method to account for phenotypic variation of the host. Together these tools highlight great potential in aiding our understanding of nematode parasitism.

Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components

BACKGROUND

Classification and phenotype identification of lettuce leaves urgently require fine quantification of their multi-semantic traits. Different components of lettuce leaves undertake specific physiological functions and can be quantitatively described and interpreted using their observable properties. In particular, petiole and veins determine mechanical support and material transport performance of leaves, while other components may be closely related to photosynthesis. Currently, lettuce leaf phenotyping does not accurately differentiate leaf components, and there is no comparative evaluation for positive-back of the same lettuce leaf. In addition, a few traits of leaf components can be measured manually, but it is time-consuming, laborious, and inaccurate. Although several studies have been on image-based phenotyping of leaves, there is still a lack of robust methods to extract and validate multi-semantic traits of large-scale lettuce leaves automatically.

RESULTS

In this study, we developed an automated phenotyping pipeline to recognize the components of detached lettuce leaves and calculate multi-semantic traits for phenotype identification. Six semantic segmentation models were constructed to extract leaf components from visible images of lettuce leaves. And then, the leaf normalization technique was used to rotate and scale different leaf sizes to the "size-free" space for consistent leaf phenotyping. A novel lamina-based approach was also utilized to determine the petiole, first-order vein, and second-order veins. The proposed pipeline contributed 30 geometry-, 20 venation-, and 216 color-based traits to characterize each lettuce leaf. Eleven manually measured traits were evaluated and demonstrated high correlations with computation results. Further, positive-back images of leaves were used to verify the accuracy of the proposed method and evaluate the trait differences.

CONCLUSIONS

The proposed method lays an effective strategy for quantitative analysis of detached lettuce leaves' fine structure and components. Geometry, color, and vein traits of lettuce leaf and its components can be comprehensively utilized for phenotype identification and breeding of lettuce. This study provides valuable perspectives for developing automated high-throughput phenotyping application of lettuce leaves and the improvement of agronomic traits such as effective photosynthetic area and vein configuration.

MultipleXLab: A high-throughput portable live-imaging root phenotyping platform using deep learning and computer vision

BACKGROUND

Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging.

RESULTS

We developed the MultipleXLab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the MultipleXLab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants.

CONCLUSION

MultipleXLab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.

Camelina sativa High-Throughput Phenotyping Under Normal and Salt Conditions Using a Plant Phenomics Platform

Climate change and environmental pollution will have a great impact on food security worldwide. More than 30% of the world's irrigated areas are estimated to be perturbed by high salinity affecting the productivity of crops. Camelina sativa, also known as false flax, is a flowering plant that is mainly cultivated as an oilseed crop that has many potential economic benefits; it can be used in food products, in industrial applications, and in animal feed and converted into biofuel. However, natural disasters due to climate events have led to significant crop losses. In this work, we developed a high-throughput phenotyping protocol to analyze the effects of different concentrations of salt on C. sativa using the McGill Plant Phenomics Platform (MP3). We present an adapted protocol to be applied with phenomics facilities in a greenhouse environment and the most effective way for high-throughput phenotyping.

CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements

The phenotypic characterization of crop genotypes is an essential, yet challenging, aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agricultural research due to the diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

Plant Leaf Detection and Counting in a Greenhouse during Day and Nighttime Using a Raspberry Pi NoIR Camera

A non-destructive method using machine vision is an effective way to monitor plant growth. However, due to the lighting changes and complicated backgrounds in outdoor environments, this becomes a challenging task. In this paper, a low-cost camera system using an NoIR (no infrared filter) camera and a Raspberry Pi module is employed to detect and count the leaves of Ramie plants in a greenhouse. An infrared camera captures the images of leaves during the day and nighttime for a precise evaluation. The infrared images allow Otsu thresholding to be used for efficient leaf detection. A combination of numbers of thresholds is introduced to increase the detection performance. Two approaches, consisting of static images and image sequence methods are proposed. A watershed algorithm is then employed to separate the leaves of a plant. The experimental results show that the proposed leaf detection using static images achieves high recall, precision, and F1 score of 0.9310, 0.9053, and 0.9167, respectively, with an execution time of 551 ms. The strategy of using sequences of images increases the performances to 0.9619, 0.9505, and 0.9530, respectively, with an execution time of 516.30 ms. The proposed leaf counting achieves a difference in count (DiC) and absolute DiC (ABS_DiC) of 2.02 and 2.23, respectively, with an execution time of 545.41 ms. Moreover, the proposed method is evaluated using the benchmark image datasets, and shows that the foreground–background dice (FBD), DiC, and ABS_DIC are all within the average values of the existing techniques. The results suggest that the proposed system provides a promising method for real-time implementation.

Image analysis for the automatic phenotyping of Orobanche cumana tubercles on sunflower roots

BACKGROUND

The parasitic plant Orobanche cumana is one of the most important threats to sunflower crops in Europe. Resistant sunflower varieties have been developed, but new O. cumana races have evolved and have overcome introgressed resistance genes, leading to the recurrent need for new resistance methods. Screening for resistance requires the phenotyping of thousands of sunflower plants to various O. cumana races. Most phenotyping experiments have been performed in fields at the later stage of the interaction, requiring time and space. A rapid phenotyping screening method under controlled conditions would need less space and would allow screening for resistance of many sunflower genotypes. Our study proposes a phenotyping tool for the sunflower/O. cumana interaction under controlled conditions through image analysis for broomrape tubercle analysis at early stages of the interaction.

RESULTS

We optimized the phenotyping of sunflower/O. cumana interactions by using rhizotrons (transparent Plexiglas boxes) in a growth chamber to control culture conditions and Orobanche inoculum. We used a Raspberry Pi computer with a picamera for acquiring images of inoculated sunflower roots 3 weeks post inoculation. We set up a macro using ImageJ free software for the automatic counting of the number of tubercles. This phenotyping tool was named RhizOSun. We evaluated five sunflower genotypes inoculated with two O. cumana races and showed that automatic counting of the number of tubercles using RhizOSun was highly correlated with manual time-consuming counting and could be efficiently used for screening sunflower genotypes at the tubercle stage.

CONCLUSION

This method is rapid, accurate and low-cost. It allows rapid imaging of numerous rhizotrons over time, and it enables image tracking of all the data with time kinetics. This paves the way toward automatization of phenotyping in rhizotrons that could be used for other root phenotyping, such as symbiotic nodules on legumes.

Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance

Near infrared (NIR) remote sensing has applications in vegetation analysis as well as geological investigations. For extra-terrestrial applications, this is particularly relevant to Moon, Mars and asteroid exploration, where minerals exhibiting spectral phenomenology between 600 and 800 nm have been identified. Recent progress in the availability of processors and sensors has created the possibility of development of low-cost instruments able to return useful scientific results. In this work, two Raspberry Pi camera types and a panchromatic astronomy camera were trialed within a pushbroom sensor to determine their utility in measuring and processing the spectrum in reflectance. Algorithmic classification of all 15 test materials exhibiting spectral phenomenology between 600 and 800 nm was easily performed. Calibration against a spectrometer considers the effects of the sensor, inherent image processing pipeline and compression. It was found that even the color Raspberry Pi cameras that are popular with STEM applications were able to record and distinguish between most minerals and, contrary to expectations, exploited the infra-red secondary transmissions in the Bayer filter to gain a wider spectral range. Such a camera without a Bayer filter can markedly improve spectral sensitivity but may not be necessary.

High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.

Species-independent analytical tools for next-generation agriculture

Innovative approaches are urgently required to alleviate the growing pressure on agriculture to meet the rising demand for food. A key challenge for plant biology is to bridge the notable knowledge gap between our detailed understanding of model plants grown under laboratory conditions and the agriculturally important crops cultivated in fields or production facilities. This Perspective highlights the recent development of new analytical tools that are rapid and non-destructive and provide tissue-, cell- or organelle-specific information on living plants in real time, with the potential to extend across multiple species in field applications. We evaluate the utility of engineered plant nanosensors and portable Raman spectroscopy to detect biotic and abiotic stresses, monitor plant hormonal signalling as well as characterize the soil, phytobiome and crop health in a non- or minimally invasive manner. We propose leveraging these tools to bridge the aforementioned fundamental gap with new synthesis and integration of expertise from plant biology, engineering and data science. Lastly, we assess the economic potential and discuss implementation strategies that will ensure the acceptance and successful integration of these modern tools in future farming practices in traditional as well as urban agriculture.

Nanobiotechnology approaches for engineering smart plant sensors

Nanobiotechnology has the potential to enable smart plant sensors that communicate with and actuate electronic devices for improving plant productivity, optimize and automate water and agrochemical allocation, and enable high-throughput plant chemical phenotyping. Reducing crop loss due to environmental and pathogen-related stresses, improving resource use efficiency and selecting optimal plant traits are major challenges in plant agriculture industries worldwide. New technologies are required to accurately monitor, in real time and with high spatial and temporal resolution, plant physiological and developmental responses to their microenvironment. Nanomaterials are allowing the translation of plant chemical signals into digital information that can be monitored by standoff electronic devices. Herein, we discuss the design and interfacing of smart nanobiotechnology-based sensors that report plant signalling molecules associated with health status to agricultural and phenotyping devices via optical, wireless or electrical signals. We describe how nanomaterial-mediated delivery of genetically encoded sensors can act as tools for research and development of smart plant sensors. We assess performance parameters of smart nanobiotechnology-based sensors in plants (for example, resolution, sensitivity, accuracy and durability) including in vivo optical nanosensors and wearable nanoelectronic sensors. To conclude, we present an integrated and prospective vision on how nanotechnology could enable smart plant sensors that communicate with and actuate electronic devices for monitoring and optimizing individual plant productivity and resource use.

Review: New sensors and data-driven approaches-A path to next generation phenomics

At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for "next generation phenomics" based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts.

SpotCard: an optical mark recognition tool to improve field data collection speed and accuracy

BACKGROUND

When taking photographs of plants in the field, it is often necessary to record additional information such as sample number, biological replicate number and subspecies. Manual methods of recording such information are slow, often involve laborious transcription from hand-written notes or the need to have a laptop or tablet on site, and present a risk by separating written data capture from image capture. Existing tools for field data capture focus on recording information rather than capturing pictures of plants.

RESULTS

We present SpotCard, a tool comprising two macros. The first can be used to create a template for small, reusable cards for use when photographing plants. Information can be encoded on these cards in a human- and machine-readable form, allowing the user to swiftly make annotations before taking the photograph. The second part of the tool automatically reads the annotations from the image and tabulates them in a CSV file, along with picture date, time and GPS coordinates. The SpotCard also provides a convenient scale bar and coordinate location within the image for the flower itself, enabling automated measurement of floral traits such as area and perimeter.

CONCLUSIONS

This tool is shown to read annotations with a high degree of accuracy and at a speed greatly faster than manual transcription. It includes the ability to read the date and time of the photograph, as well as GPS location. It is an open-source ImageJ/Fiji macro and is available online. Its use requires no knowledge of the ImageJ macro coding language, and it is therefore well suited to all researchers taking pictures in the field.

Evaluation of Strategies for the Development of Efficient Code for Raspberry Pi Devices

The Internet of Things (IoT) is faced with challenges that require green solutions and energy-efficient paradigms. Architectures (such as ARM) have evolved significantly in recent years, with improvements to processor efficiency, essential for always-on devices, as a focal point. However, as far as software is concerned, few approaches analyse the advantages of writing efficient code when programming IoT devices. Therefore, this proposal aims to improve source code optimization to achieve better execution times. In addition, the importance of various techniques for writing efficient code for Raspberry Pi devices is analysed, with the objective of increasing execution speed. A complete set of tests have been developed exclusively for analysing and measuring the improvements achieved when applying each of these techniques. This will raise awareness of the significant impact the recommended techniques can have.