Smartphone-based detection of leaf color levels in rice plants

Tao, Ming; Xu, Ma; Huang, Xiaoping; Deng, Ruoling; Liang, Kun; Qi, Long

doi:10.1016/j.compag.2020.105431

Cited by 45 publications

(23 citation statements)

References 30 publications

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“…Visual monitoring ranges from custom-made devices such as LeafSpec [26,27], the use of a normal camera combined with a microcontroller, a processor board [28][29][30] or a smartphone camera [31][32][33]. Papers propose monitoring plants with different types of cameras: standard spectral camera, infrared camera, thermal imaging camera, or color component camera.…”

Section: Sensorsmentioning

confidence: 99%

Developing a Modern Greenhouse Scientific Research Facility—A Case Study

Cafuta

Dodig

Cesar

et al. 2021

Sensors

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Multidisciplinary approaches in science are still rare, especially in completely different fields such as agronomy science and computer science. We aim to create a state-of-the-art floating ebb and flow system greenhouse that can be used in future scientific experiments. The objective is to create a self-sufficient greenhouse with sensors, cloud connectivity, and artificial intelligence for real-time data processing and decision making. We investigated various approaches and proposed an optimal solution that can be used in much future research on plant growth in floating ebb and flow systems. A novel microclimate pocket-detection solution is proposed using an automatically guided suspended platform sensor system. Furthermore, we propose a methodology for replacing sensor data knowledge with artificial intelligence for plant health estimation. Plant health estimation allows longer ebb periods and increases the nutrient level in the final product. With intelligent design and the use of artificial intelligence algorithms, we will reduce the cost of plant research and increase the usability and reliability of research data. Thus, our newly developed greenhouse would be more suitable for plant growth research and production.

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Section: Sensorsmentioning

confidence: 99%

Developing a Modern Greenhouse Scientific Research Facility—A Case Study

Cafuta

Dodig

Cesar

et al. 2021

Sensors

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“…The likely reason for this observation could be that nitrogen fertilizer applications increased the amount of available nitrogen in the soil which plants absorbed immediately (Figure 3). The leaf colour of the plants helps the farmers know the nitrogen content in the leaves and understand when to apply fertilizer (Tao et al, 2020). Studies have concluded that the level of leaf nitrogen is affected by the rate of nitrogen application (Taylaran et al, 2011).…”

Section: Leaf Colormentioning

confidence: 99%

Effect of Ammonium Sulfate Application Levels on the Growth and Yield of IR-28 Rice

Ndille

Ballah

Safi³

et al. 2021

AJAFS

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The study was conducted in JICA Tsukuba experimental rice field RE-2 from April 2018 to September 2018, to determine the effect of different levels of nitrogen fertilizer (Ammonium sulfate) application, on the growth and the yield of IR-28 rice. Four levels of nitrogen fertilizer were applied; 0kg.ha-1 (N0), 40kg.ha-1(N40), 60kg.ha-1(N60) and 80kg.ha-1(N80). For each of the four levels, part of the fertilizer was applied as basal dressing prior to transplanting, and the rest was applied as top-dressing at the panicle initiation stage. The experimental design was a Randomized Complete Block Design (RCBD) with four treatments and three replications. The plant length, the tiller number and the leaf colour were measured for growth data. The number of panicles per m2, the number of spikelets per panicle, the spikelet fertility rate, the 1000 grains weight, and the calculated yield were determined for yield components assessment. The plant length and the tiller number were significantly higher in N80 and N60 compared to N40 and N0, and N80 showed the highest values. There was no significant difference among the four nitrogen levels in terms of the number of panicles per m2 and the number of spikelets per panicle. The spikelet fertility rate and the 1000 grains weight were significantly higher in N80, N60 and N40 compared to N0, and no significant difference was observed among the three. Calculated yield values were higher in N80 (5.74 tons.ha-1) and N60 (5.38 tons.ha-1) compared to N40(4.88 tons.ha-1) and N0 (4.36 tons.ha-1), but there were no significant differences among the four treatments (5% Level of HSD). These results suggest that a high yield of rice can be achieved through the application of high amounts of nitrogen fertilizers. N60 nitrogen level can be recommended for optimum yield of IR-28. Although N80 showed higher yield and yield components, N60 is the best and the most economical nitrogen level required for optimum yield of IR-28.

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“…Smartphones also have enough computing power to process the captured data, which enables rapid assessments of plant stresses. However, many factors can complicate RGB data, such as lighting, environmental conditions, time of day, and spectral resolution [64,65]. Illumination is a particularly important concern in terms of field applications since it can vary greatly depending on the season and weather conditions.…”

Section: Rgb Imagingmentioning

confidence: 99%

“…Smartphone being used to evaluate the leaf color of rice plant leaves, which is applicable in detecting nitrogen deficiencies. Reprinted with permission from[65]. ©2020 Elsevier.Biosensors 2020, 10, x FOR PEER REVIEW 8 of 28…”

mentioning

confidence: 99%

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Zubler

Yoon

2020

Biosensors

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Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

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Smartphone-based detection of leaf color levels in rice plants

Cited by 45 publications

References 30 publications

Developing a Modern Greenhouse Scientific Research Facility—A Case Study

Developing a Modern Greenhouse Scientific Research Facility—A Case Study

Effect of Ammonium Sulfate Application Levels on the Growth and Yield of IR-28 Rice

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

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