Monitoring Rice Crop and Paddy Field Condition Using UAV RGB Imagery

Sari, Mohd Yazid Abu; Hassim, Yana Mazwin Mohmad; Hidayat, Rahmat; Ahmad, Aasim

doi:10.30630/joiv.5.4.742

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…Therefore, this model can be used to verify the correctness of varieties in the inspection process of plantings in the context of seed certification. This is in line with what was stated by Sari et al (2021) that imagery from drones (RGB) can be used in rice monitoring. The CNN model is also reported for the detection and classification of plant pests and diseases (Domingues et al, 2022) and corn seeds in the context of testing purity (Bi et al, 2022).…”

Section: Cnn Model Evaluationsupporting

confidence: 92%

Rice variety identification system based on drone images to support seed certification process

Ardiyansah¹,

Zamzami²,

Wulandari³

2023

Indonesian J. Agron.

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Utilization of technology can be a solution in the process of supervising certified seeds, especially at the stage of field inspection, which is faster and more efficient. This study aimed to develop a drone image-based rice variety system to support the inspection process for seed certification. The research was conducted from March – July 2022. The rice plants of IPB 3S and Inpari 32 varieties located in Karawang, West Java were observed for their agronomic characteristics. The images of the two varieties were taken using a drone and augmented and cropped. The overall image obtained was 80% used as training data, 20% as data validation, and 10% as test data. The variety identification system was built using a model by applying the convolutional neural network (CNN) algorithm. The performance of the model was observed through accuracy, precision, recall, and F1-Score. All agronomic characters justified that the two varieties used were different. This study produced three CNN models that could accurately identify the varieties of IPB 3S and Inpari 32 with an accuracy rate of 99.52% to 100%. Drone imaging is prospective for field inspection process of seed certification. Keywords: CNN, deep learning, image processing, seed production, unmanned aerial vehicle

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Section: Cnn Model Evaluationsupporting

confidence: 92%

Rice variety identification system based on drone images to support seed certification process

Ardiyansah¹,

Zamzami²,

Wulandari³

2023

Indonesian J. Agron.

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“…The farm manager can now view the entire paddy field using aerial imagery without missing any locations. Therefore, aerial imagery using multispectral sensors can assist farm management in constantly monitoring paddy with ground surveying for validation (Lu et al, 2021;Sari et al, 2021).…”

Section: Paddy Growth Monitoring Based On Aerial Imagery Generated Fr...mentioning

confidence: 99%

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

Mohidem,

Jaafar,

Che’Ya

2024

JST

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Using a conventional approach via visual observation on the ground, farmers encounter difficulties monitoring the entire paddy field area, and it is time-consuming to do manually. The application of unmanned aerial vehicles (UAVs) could help farmers optimise inputs such as water and fertiliser to increase yield, productivity, and quality, allowing them to manage their operations at lower costs and with minimum environmental impact. Therefore, this article aims to provide an overview of the integration of UAV and multispectral sensors in monitoring paddy growth applications based on vegetation indices and soil plant analysis development (SPAD) data. The article briefly describes current rice production in Malaysia and a general concept of precision agriculture technologies. The application of multispectral sensors integrated with UAVs in monitoring paddy growth is highlighted. Previous research on aerial imagery derived from the multispectral sensor using the normalised difference vegetation index (NDVI) is explored to provide information regarding the health condition of the paddy. Validation of the paddy growth map using SPAD data in determining the leaf’s relative chlorophyll and nitrogen content is also being discussed. Implementation of precision agriculture among low-income farmers could provide valuable insights into the practical implications of this review. With ongoing education, training and experience, farmers can eventually manage the UAV independently in the field. This article concludes with a future research direction regarding the production of growth maps for other crops using a variety of vegetation indices and map validation using the SPAD metre values.

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“…To secure rice productivity, early monitoring is important to identify the crop condition, to improve rice crop management, and eventually to estimate rice production (Sari et al, 2021). Rice plant characteristics that can be monitored are plant height (Muangprakhon & Kaewplang, 2021), chlorophyll content index (Ban et al, 2022;Wang et al, 2023), number of tillers (Munibah et al, 2022) and leaf area index (LAI) (Gong et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation (Lillesand et al, 2014). Unmanned Aerial Vehicle (UAV) is one remote sensing platform to collect data and monitor crop conditions with high spatiotemporal resolution imagery (Duan et al, 2019;Sari et al, 2021;Bascon et al, 2022). UAVs have abilities to acquire the earth's surface with low flight (more detail) and short revisit time on the same location (more database).…”

Section: Introductionmentioning

confidence: 99%

Application of RGB UAV images to identify spectral patterns and estimate rice production

Munibah,

Iskandar,

Barus

et al. 2024

J. Agron. Indonesia (Indonesian J. Agron.)

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Monitoring rice plant growth is crucial for evaluating rice field management and yield production. RGB images are generated from Unmanned Aerial Vehicles (UAV) with RGB cameras. UAVs produce high spatial and temporal resolution, while RGB cameras are commonly used and cheap. The objectives of this study were to identify the spectral pattern of rice plant growth and to estimate yield production based on the spectral value of RGB images. The spectral pattern and yield estimation were analyzed using confidence interval (CI) and regression, respectively. Results show that spectral pattern during the vegetative until ripening stage forms a concave with minimum value in the generative stage and decreases towards the harvest stage. Based on the CI value, the high interval between upper and lower happened in the vegetative and ripening stages while the low interval happened in the generative stage. The high CI in the vegetative and ripening stages was due to the soil background and complexity of the rice plant canopy, respectively while the low CI in the generative stage was due to the homogeneous response of the leaf canopy. The best rice yield estimation based on the spectral value occurs in the ripening stage with an R2 of 0.84. Keywords: chlorophyll content, confidence interval, drone images, rice plant, regression

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Monitoring Rice Crop and Paddy Field Condition Using UAV RGB Imagery

Cited by 7 publications

References 24 publications

Rice variety identification system based on drone images to support seed certification process

Rice variety identification system based on drone images to support seed certification process

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

Application of RGB UAV images to identify spectral patterns and estimate rice production

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