CIG based Stress Identification Method for Maize Crop using UAV based Remote Sensing

Kumar, Ajay; Taparia, Mahesh; Rajalakshmi, P.; Guo, Wei; Balaji, Bharadwaj Bagepalli; Marathi, Balram; Desai, U.B.

doi:10.1109/sas48726.2020.9220016

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…In this study, the Chlorophyll Index-Green (CIG) was used to assess changes in chlorophyll content during the sunflower growth cycle and identify stressed areas in the field. The CIG is calculated by subtracting 1 from the ratio of the Near-Infrared (NIR) band to the Green band (Kumar et al, 2020). Figure 6 illustrates the CIG results, showing noticeable variations in leaf greenness between stressed and healthy sunflower plants in the multitemporal Sentinel-2 dataset.…”

Section: Application and Resultsmentioning

confidence: 99%

Assessing the Impact of Beet Webworm Moths on Sunflower Fields Using Multitemporal Sentinel-2 Satellite Imagery and Vegetation Indices

Kara,

Maden,

Ercan

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Remote sensing technology plays a crucial role in detecting and monitoring environmental issues, offering the ability to monitor large areas, diagnose problems early, and facilitate accurate interventions. By integrating in-situ data with qualitative measurements obtained from satellite images, comprehensive insights can be obtained, and statistical inferences can be established. This study focuses on analyzing the damages caused by beet webworm moths (Loxostege sticticalis) in sunflower fields located in the Ortaca neighborhood of Tekirdağ province in Thrace region, utilizing Sentinel-2 satellite images and in-situ data collected from the sunflower fields in Ortaca. The relationship between different spectral indices, such as the Enhanced Vegetation Index, Chlorophyll Index Green, and spectral transformation techniques like Tasseled Cap Greenness, derived from Sentinel-2 satellite images, and the observed damage rates in various sunflower fields' in-situ data was investigated. The results revealed a negative correlation between the variables, highlighting EVI as the most effective indicator of damage among the plant indices. Leveraging these findings, a damage map was generated using EVI, enabling visual interpretation of the damage status in other sunflower fields within the study area. These findings offer valuable insights into the impact of pests on sunflower crops, despite the accuracy evaluation results falling below the desired level, with an overall accuracy of 75% and a Kappa accuracy of 65%, attributed to the limited availability of in-situ data.

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Section: Application and Resultsmentioning

confidence: 99%

Assessing the Impact of Beet Webworm Moths on Sunflower Fields Using Multitemporal Sentinel-2 Satellite Imagery and Vegetation Indices

Kara,

Maden,

Ercan

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The result of the study shows it can save time and provide more reliable results than human visual work [97]. Kumar et al [98] used a multi-spectral image sensor mounted on an unmanned aerial vehicle (UAV) to get the near-infrared, green, and red band images, which were applied to achieve the vegetative index for maize crop health monitoring. The study results show that this method successfully detects the water-stressed area.…”

Section: Multi-spectral Image Sensormentioning

confidence: 99%

“…The study results show that this method successfully detects the water-stressed area. The irrigation process and crop health monitoring have been optimized through this study [98]. Mardanisamani et al [99] applied a multi-spectral image sensor with five spectral channels (red, blue, green, near the infrared, and red edge) on a quadcopter to get the images of wheat and canola from two breeding field trials.…”

Section: Multi-spectral Image Sensormentioning

confidence: 99%

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A Review on Sensing Technologies for High-Throughput Plant Phenotyping

Rayhana

Feng

et al. 2022

IEEE Open J. Instrum. Meas.

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The current epidemic, population growth, and decreasing arable lands lead to a severe food crisis, which calls for productive and efficient agricultural methods to ensure a sustainable food supply for mankind. Crop monitoring is considered to be a potential solution for the improvement of food production. Current crop monitoring combines agriculture methodologies with other advanced technologies, including sensing technology, geographical information systems (GIS), Internet of Things (IoT), information and communication technology (ICT), robotics, and drone techniques to increase production with low labor cost. The high-throughput plant phenotyping is crucial for crop monitoring on the data acquisition of large-scale crop characteristics. The high-throughput plant phenotyping studies aim to achieve fast and precise large-scale crop monitoring techniques with minimum environmental impact by applying special plant phenotyping platforms. The phenotyping platforms are integrated with various sensors and data communication systems, which can help to achieve automatic data acquisition and transmission. This paper reviews the current high-throughput plant phenotyping development in crop monitoring, including sensors, communication protocols, data management, and plant phenotyping platforms. State-of-art challenges are reviewed and discussed. Also, the paper provides discussions on the current situation, upcoming challenges, and possible future trends for researchers in this field.

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A Comprehensive Review of Emerging Technologies: Machine Learning and UAV in Crop Management

Yagnasree

Jain

2022

J. Phys.: Conf. Ser.

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It is a known fact that India is one among the major food producers in the world. Due to the rapid urbanization and Agrochemical interventions, plants are infected by insects, pathogens, massive infestations, which leads to the deficiency of growth nutrients and ions, thereby minimizing agricultural crop yield. These infections occur in several forms like aphids, black root rot, cankers, leaf curls, rusts, anthracnose, and blights make agriculture fail to thrive and increase the impact of crop diseases. Since plant disease prevention is a continuous process that occurs regularly, agriculturists follow several strategies to keep their crops disease-free. An efficient monitoring and supporting system for continuous and long-term plant and soil health monitoring is needed to meet the needs of growing population. In this paper, existing research works in Precision agriculture, emerging technologies – GPS, GIS, Machine learning and UAVs in analyzing crop health analysis, soil health monitoring, and crop yield prediction are reviewed.

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CIG based Stress Identification Method for Maize Crop using UAV based Remote Sensing

Cited by 4 publications

References 24 publications

Assessing the Impact of Beet Webworm Moths on Sunflower Fields Using Multitemporal Sentinel-2 Satellite Imagery and Vegetation Indices

Assessing the Impact of Beet Webworm Moths on Sunflower Fields Using Multitemporal Sentinel-2 Satellite Imagery and Vegetation Indices

A Review on Sensing Technologies for High-Throughput Plant Phenotyping

A Comprehensive Review of Emerging Technologies: Machine Learning and UAV in Crop Management

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