In vivo human-like robotic phenotyping of leaf traits in maize and sorghum in greenhouse

Atefi, Abbas; Ge, Yufeng; Pitla, Santosh K.; Schnable, James C.

doi:10.1016/j.compag.2019.104854

Cited by 28 publications

(19 citation statements)

References 19 publications

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“…In our previous study [20], an image processing-based algorithm was developed to detect the stem in the image. However, this method had limited success with tilted stems (see Supplementary Figure S1).…”

Section: Stem Detection Using Deep Learning Technique (Faster R-cnn)mentioning

confidence: 99%

“…Therefore, deep learning technique was used to overcome this issue in this research. For this purpose, sixty grey-scale TOF images (Z direction/depth images) from the previous study [20] were used. It was speculated that they have more information (8-bit images) which can help in stem detection model calibrated by deep learning compared to binary images.…”

Section: Stem Detection Using Deep Learning Technique (Faster R-cnn)mentioning

confidence: 99%

“…The training set was used to train the Faster R-CNN model and find the weights and biases of the network, and the testing set was used to evaluate the performance of the network. In our previous study [20], an image processing-based algorithm was developed to detect the stem in the image. However, this method had limited success with tilted stems (see Supplementary Figure S1).…”

Section: Stem Detection Using Deep Learning Technique (Faster R-cnn)mentioning

confidence: 99%

“…The results of outdoor experiments demonstrated that the algorithm for stem detection worked well and all identified stems were grasped by the gripper successfully. Atefi et al [20] developed a plant phenotyping robotic system to realize automated measurement of leaf reflectance and leaf temperature of maize and sorghum plants in greenhouse. The robotic system consisted of a TOF camera, a four DOF robotic manipulator, an optical fiber cable (coupled to a portable spectrometer) and a thermistor for leaf spectral reflectance and leaf temperature measurements, and a custom-designed gripper to integrate the sensors to the end-effector of the robotic manipulator.…”

Section: Introductionmentioning

confidence: 99%

“…Our previous work [20] focused on the automated grasping leaves and measuring their properties. As a continuation, this work focuses on developing a robotic phenotyping system which has the capability to detect and physically grasp the stem of maize and sorghum.…”

Section: Introductionmentioning

confidence: 99%

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Robotic Detection and Grasp of Maize and Sorghum: Stem Measurement with Contact

Atefi

Pitla

et al. 2020

Robotics

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Frequent measurements of the plant phenotypes make it possible to monitor plant status during the growing season. Stem diameter is an important proxy for overall plant biomass and health. However, the manual measurement of stem diameter in plants is time consuming, error prone, and laborious. The use of agricultural robots to automatically collect plant phenotypic data for trait measurements can overcome many of the drawbacks of manual phenotyping. The objective of this research was to develop a robotic system that can automatically detect and grasp the stem, and measure its diameter of maize and sorghum plants. The robotic system comprises of a four degree of freedom robotic manipulator, a time-of-flight camera for vision system, and a linear potentiometer sensor to measure the stem diameter. Deep learning and conventional image processing were used to detect stem in images and find grasping point of stem, respectively. An experiment was conducted in a greenhouse using maize and sorghum plants to evaluate the performance of the robotic system. The system demonstrated successful grasping of stem and a high correlation between manual and robotic measurements of diameter depicting its ability to be used as a prototype to integrate other sensors to measure different physiological and chemical attributes of the stem.

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Section: Stem Detection Using Deep Learning Technique (Faster R-cnn)mentioning

confidence: 99%

Section: Stem Detection Using Deep Learning Technique (Faster R-cnn)mentioning

confidence: 99%

Section: Stem Detection Using Deep Learning Technique (Faster R-cnn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robotic Detection and Grasp of Maize and Sorghum: Stem Measurement with Contact

Atefi

Pitla

et al. 2020

Robotics

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Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

Karanisa

Achour²,

Ouammi

et al. 2022

Environ Syst Decis

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Greenhouse farming is essential in increasing domestic crop production in countries with limited resources and a harsh climate like Qatar. Smart greenhouse development is even more important to overcome these limitations and achieve high levels of food security. While the main aim of greenhouses is to offer an appropriate environment for high-yield production while protecting crops from adverse climate conditions, smart greenhouses provide precise regulation and control of the microclimate variables by utilizing the latest control techniques, advanced metering and communication infrastructures, and smart management systems thus providing the optimal environment for crop development. However, due to the development of information technology, greenhouses are undergoing a big transformation. In fact, the new generation of greenhouses has gone from simple constructions to sophisticated factories that drive agricultural production at the minimum possible cost. The main objective of this paper is to present a comprehensive understanding framework of the actual greenhouse development in Qatar, so as to be able to support the transition to sustainable precision agriculture. Qatar’s greenhouse market is a dynamic sector, and it is expected to mark double-digit growth by 2025. Thus, this study may offer effective supporting information to decision and policy makers, professionals, and end-users in introducing new technologies and taking advantage of monitoring techniques, artificial intelligence, and communication infrastructure in the agriculture sector by adopting smart greenhouses, consequently enhancing the Food-Energy-Water Nexus resilience and sustainable development. Furthermore, an analysis of the actual agriculture situation in Qatar is provided by examining its potential development regarding the existing drivers and barriers. Finally, the study presents the policy measures already implemented in Qatar and analyses the future development of the local greenhouse sector in terms of sustainability and resource-saving perspective and its penetration into Qatar’s economy.

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