Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks

2021 ◽  
Vol 92 ◽  
pp. 107098
Author(s):  
Ajay Arunachalam ◽  
Henrik Andreasson
Inventions ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 42
Author(s):  
Worasit Sangjan ◽  
Arron H. Carter ◽  
Michael O. Pumphrey ◽  
Vadim Jitkov ◽  
Sindhuja Sankaran

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.


2020 ◽  
Vol 35 (1-2) ◽  
Author(s):  
Alka Arora ◽  
Tanuj Mishra ◽  
Sudeep Marwaha ◽  
Mrinmoy Ray ◽  
R. S. Tomar

Plant biomass is the basis for the calculation of net primary production. Estimation of fresh biomass in high throughput way is critical for plant phenotyping. Conventional phenotyping approaches for measuring the fresh biomass is time consuming, laborious and destructive in nature. Image analysis based plant phenotyping is very popular nowadays. Most of the approaches used projected shoot area from visual images (VIS) to estimate the fresh biomass. As water content has a significant effect on fresh biomass and water absorbs radiation at near infra-red (NIR) region (900nm to 1700nm), we have hypothesized that the combined use of VIS and NIR imaging can predict the fresh biomass more accurately that the VIS image alone. In this study, VIS and NIR images were collected using LemaTec facility installed at Nanaji Deshmukh Plant Phenomics Center, ICAR-IARI, New Delhi-12. In this study, VIS and NIR imaging were captured for rice leaves with different moisture content as a test case. MATLAB software (version 2015b) was used for image analysis. The two image derived parameter viz. Green Leaf Proportion (GPR) from VIS image and mean gray value/intensity (MGV_NIR) from NIR image were used to develop the statistical model to estimate the fresh biomass in the form of Leaf Fresh Weight (LFW). The proposed approach significantly enhanced the fresh biomass estimation.


2018 ◽  
Author(s):  
Oliver L Tessmer ◽  
David M Kramer ◽  
Jin Chen

AbstractThere is a critical unmet need for new tools to analyze and understand “big data” in the biological sciences where breakthroughs come from connecting massive genomics data with complex phenomics data. By integrating instant data visualization and statistical hypothesis testing, we have developed a new tool called OLIVER for phenomics visual data analysis with a unique function that any user adjustment will trigger real-time display updates for any affected elements in the workspace. By visualizing and analyzing omics data with OLIVER, biomedical researchers can quickly generate hypotheses and then test their thoughts within the same tool, leading to efficient knowledge discovery from complex, multi-dimensional biological data. The practice of OLIVER on multiple plant phenotyping experiments has shown that OLIVER can facilitate scientific discoveries. In the use case of OLIVER for large-scale plant phenotyping, a quick visualization identified emergent phenotypes that are highly transient and heterogeneous. The unique circular heat map with false-color plant images also indicates that such emergent phenotypes appear in different leaves under different conditions, suggesting that such previously unseen processes are critical for plant responses to dynamic environments.


2021 ◽  
Author(s):  
Geldhof Batist ◽  
Pattyn Jolien ◽  
Eyland David ◽  
Carpentier Sebastien ◽  
Van de Poel Bram

Abstract Plant and plant organ movements are the result of a complex integration of endogenous growth and developmental responses, partially controlled by the circadian clock, and external environmental cues. Monitoring of plant motion is typically done by image-based phenotyping techniques with the aid of computer vision algorithms. Here we present a method to measure leaf movements using a digital inertial measurement unit (IMU) sensor. The lightweight sensor is easily attachable to a leaf or plant organ and records angular traits in real-time for two dimensions (pitch and roll) with high resolution (measured sensor oscillations of 0.36° ± 0.53° for pitch and 0.50° ± 0.65° for roll). We were able to record simple movements such as petiole bending, as well as complex lamina motions, in several crops, ranging from tomato to banana. We also assessed growth responses in terms of lettuce rosette expansion and maize seedling stem movements. The IMU sensors are capable of detecting small changes of nutations (i.e., bending movements) in leaves of different ages and in different plant species. In addition, the sensor system can also monitor stress-induced leaf movements. We observed that unfavorable environmental conditions evoke certain leaf movements, such as drastic epinastic responses, as well as subtle fading of the amplitude of nutations. In summary, the presented digital sensor system enables continuous detection of a variety of leaf motions with high precision, and is a low-cost tool in the field of plant phenotyping, with potential applications in early stress detection.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Janni Michela ◽  
Cocozza Claudia ◽  
Brilli Federico ◽  
Pignattelli Sara ◽  
Vurro Filippo ◽  
...  

AbstractOne of the main impacts of climate change on agriculture production is the dramatic increase of saline (Na+) content in substrate, that will impair crop performance and productivity. Here we demonstrate how the application of smart technologies such as an in vivo sensor, termed bioristor, allows to continuously monitor in real-time the dynamic changes of ion concentration in the sap of Arundo donax L. (common name giant reed or giant cane), when exposed to a progressive salinity stress. Data collected in vivo by bioristor sensors inserted at two different heights into A. donax stems enabled us to detect the early phases of stress response upon increasing salinity. Indeed, the continuous time-series of data recorded by the bioristor returned a specific signal which correlated with Na+ content in leaves of Na-stressed plants, opening a new perspective for its application as a tool for in vivo plant phenotyping and selection of genotypes more suitable for the exploitation of saline soils.


2018 ◽  
Vol 27 (1) ◽  
Author(s):  
Erik Alexandersson ◽  
Markku Keinänen ◽  
Aakash Chawade ◽  
Kristiina Himanen

Plant phenomics refers to the systematic study of plant phenotypes. Together with closely monitored, controlled climates, it provides an essential component for the integrated analysis of genotype-phenotype-environment interactions. Currently, several plant growth and phenotyping facilities are under establishment globally, and numerous facilities are already in use. Alongside the development of the research infrastructures, several national and international networks have been established to support shared use of the new methodology. In this review, an overview is given of the Nordic plant phenotyping and climate control facilities. Since many areas of phenomics such as sensor-based phenotyping, image analysis and data standards are still developing, promotion of educational and networking activities is especially important. These facilities and networks will be instrumental in tackling plant breeding and plant protection challenges. They will also provide possibilities to study wild species and their ecological interactions under changing Nordic climate conditions.


Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4363
Author(s):  
Shona Nabwire ◽  
Hyun-Kwon Suh ◽  
Moon S. Kim ◽  
Insuck Baek ◽  
Byoung-Kwan Cho

Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.


1979 ◽  
Vol 44 ◽  
pp. 41-47
Author(s):  
Donald A. Landman

This paper describes some recent results of our quiescent prominence spectrometry program at the Mees Solar Observatory on Haleakala. The observations were made with the 25 cm coronagraph/coudé spectrograph system using a silicon vidicon detector. This detector consists of 500 contiguous channels covering approximately 6 or 80 Å, depending on the grating used. The instrument is interfaced to the Observatory’s PDP 11/45 computer system, and has the important advantages of wide spectral response, linearity and signal-averaging with real-time display. Its principal drawback is the relatively small target size. For the present work, the aperture was about 3″ × 5″. Absolute intensity calibrations were made by measuring quiet regions near sun center.


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