Automation in plant growth monitoring using high-precision image classification and virtual height measurement techniques

Akila, I.S.; Sivakumar, Arun Narenthiran; Swaminathan, S.

doi:10.1109/iciiecs.2017.8275862

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…Most of the works in the literature focus on detecting objects and not on monitoring of objects, which is what this current paper is emphasizing. For monitoring, there are works that keep a register of the size of plants [20], controlling diseases [44], environmental control [11], tracking the count of flowers and tomatoes [40,45], and keeping a registry of the volume of tomatoes [46]. This last work of Fukui et al [46] is the closest to the current paper, and one important point to notice is that the authors of Reference [46] achieved good results in a controlled environment inside a laboratory, whereas, in outside environments, the results were not as expected.…”

Section: Discussionmentioning

confidence: 99%

“…Kala et al [19] implemented a SVM (Support Vector Machine) for the detection of plant diseases and optimal application of insecticides. Akila et al [20] implemented CRFT (Conditional Random Field Temporal Search) to detect plants and monitor their growth. Sudhesh et al [21] carried out a review on recognition, categorization, and quantification of diseases in various agricultural plants through different computational methods.…”

Section: Introductionmentioning

confidence: 99%

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Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing

et al. 2021

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This work presents an alternative method, referred to as Productivity Index or PI, to quantify the production of hydroponic tomatoes using computer vision and neural networks, in contrast to other well-known metrics, such as weight and count. This new method also allows the automation of processes, such as tracking of tomato growth and quality control. To compute the PI, a series of computational processes are conducted to calculate the total pixel area of the displayed tomatoes and obtain a quantitative indicator of hydroponic crop production. Using the PI, it was possible to identify objects belonging to hydroponic tomatoes with an error rate of 1.07%. After the neural networks were trained, the PI was applied to a full crop season of hydroponic tomatoes to show the potential of the PI to monitor the growth and maturation of tomatoes using different dosages of nutrients. With the help of the PI, it was observed that a nutrient dosage diluted with 50% water shows no difference in yield when compared with the use of the same nutrient with no dilution.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing

et al. 2021

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“…These environmental parameters indirectly indicate the process of plant growth, and they cannot record the visual scenes on plant growth, resulting in the unavailability of the physical structure parameters on plants. To realize the visual monitoring of plants, some works have started to integrate the visual sensors into the plant monitoring system, e.g., Peng et al ( 2022 ) used the binocular camera to capture video sequences on a plant and used the structure from motion method (Piermattei et al, 2019 ) to extract the 3-D information of a plant; Sajith et al ( 2019 ) designed a complex network to derive the plant growth parameters from the monitoring images; Akila et al ( 2017 ) extracted the plant color and texture by the visual monitoring system. From the above, it can be seen that the visual sensor or camera is used to capture the video sequences on plant growth, and these video sequences are compressed as bitstream which is transmitted to the IoT cloud for further analyzing.…”

Section: Related Workmentioning

confidence: 99%

Green Visual Sensor of Plant: An Energy-Efficient Compressive Video Sensing in the Internet of Things

Yang

Sun

2022

Front. Plant Sci.

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Internet of Things (IoT) realizes the real-time video monitoring of plant propagation or growth in the wild. However, the monitoring time is seriously limited by the battery capacity of the visual sensor, which poses a challenge to the long-working plant monitoring. Video coding is the most consuming component in a visual sensor, it is important to design an energy-efficient video codec in order to extend the time of monitoring plants. This article presents an energy-efficient Compressive Video Sensing (CVS) system to make the visual sensor green. We fuse a context-based allocation into CVS to improve the reconstruction quality with fewer computations. Especially, considering the practicality of CVS, we extract the contexts of video frames from compressive measurements but not from original pixels. Adapting to these contexts, more measurements are allocated to capture the complex structures but fewer to the simple structures. This adaptive allocation enables the low-complexity recovery algorithm to produce high-quality reconstructed video sequences. Experimental results show that by deploying the proposed context-based CVS system on the visual sensor, the rate-distortion performance is significantly improved when comparing it with some state-of-the-art methods, and the computational complexity is also reduced, resulting in a low energy consumption.

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“…Saputra et al [9] mengembangkan sebuah sistem pemantauan pertumbuhan berdasarkan beberapa citra yang diambil dari berbagai sudut sebagai masukannya. Sementara itu, penelitian yang dilakukan oleh Akila et al [10] dan Lin et al [11] sama-sama membangun suatu sistem otomatis pengukuran ketinggian dan pertumbuhan tanaman. Penelitian ini sendiri mengusulkan sebuah sistem cerdas yang mampu memantau pertumbuhan tanaman anggrek berdasarkan citra digital secara real-time.…”

Section: Pendahuluanunclassified

Sistem Pemantauan Pertumbuhan Anggrek Berdasarkan Pengolahan Citra Digital

Satriorini

Sumiharto

Hujja

2022

Indonesian J. Electron. Instrum. Syst.

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Growth monitoring and plant protectionis the major aspect of horticulture because its productivity depends on the health of the plants. Manual direct measurement methods tend to be destructive towards observed plants. In this research, a smart non-contact growth monitoring system was implemented on a chamber with orchid plants asthe objectsobserved. The images of the orchids were taken and became the input of the system to be processed to estimate the height of the plants.The contour of the orchid plant as the object was obtained and the height was calculated based on the highest and the lowest contour.The result shows that the developed system is proven to be capable of measuring orchid’s height in real-time with accuracy more than 95,7%. Thus, this system will effectively help farmers to improve the quality and the quantity of the plant’s productivity.

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Automation in plant growth monitoring using high-precision image classification and virtual height measurement techniques

Cited by 9 publications

References 5 publications

Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing

Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing

Green Visual Sensor of Plant: An Energy-Efficient Compressive Video Sensing in the Internet of Things

Sistem Pemantauan Pertumbuhan Anggrek Berdasarkan Pengolahan Citra Digital

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