Brown Planthopper Damage Detection using Remote Sensing and Machine Learning

Kugathasan, Kumaran; Nanayakkara, Vishaka; Jayasena, HS; Perera, Amal Shehan; Fernando, L Teixeira

doi:10.1109/icmla.2019.00024

Cited by 15 publications

(16 citation statements)

References 31 publications

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“…Remotely sensed, vegetation indices and climate data are commonly used to predict paddy rice yield estimation [34], [35], [48], [76], [77], [109] and to monitor paddy rice growth [63], [73], [117] using artificial neural networks and its variants and also linear regression approaches. In addition to that, hyperspectral and high-resolution images have been used to accurately and affectively monitor paddy rice disease [40], [41], [87], [88], [119] and assessing quality of paddy rice [93], [104], [105] by using deep learning algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…At the same time, timely and accurate diagnosis of paddy diseases and managing pests are highly required to reduce loses. Monitoring paddy rice disease involves activities such as detection and recognition of diseases from paddy plant leaf images [37], [39] or classifying, detecting, and predicting infestation patterns of the Brown Planthopper in rice paddies [40], [41]. Generally, monitoring the growth of paddy rice involves analyzing the growth of paddy rice based on climate data or remotely sensed data and vegetation indices.…”

Section: Phases and Tasks In Paddy Rice Smart Farmingmentioning

confidence: 99%

“…Remotely Sensed Data and Vegetation Indices: Hyperspectral Images (Band 1 ∼ 4) [80], [81], C-Band Synthetic Aperture Radar (SAR) [40] Drones Based Data: High-resolution Images [37], [39], [87]- [92], [118], [119] Monitoring paddy rice growth Remotely Sensed Data and Vegetation Indices: LSWI [63],EVI [62], [63], NDVI [63], [66], [69], LAI [69], [71]- [73], C-Band Synthetic Aperture Radar (SAR) [83] Assessing quality of paddy rice Based on these spectral bands, several measurements can be derived and computed such as Land Surface Water Index (LSWI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) (see Table 2).…”

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

“…C-Band SAR data has been used in a wide range of applications that include sea and land monitoring. For instance, C-Band SAR has been used in predicting paddy rice yield estimation [82], monitoring paddy rice growth [83] and monitoring paddy rice disease [40].…”

Section: Tasks Types Of Features and Studiesmentioning

confidence: 99%

“…Some combined approaches show promising results that involve deep learning approaches [40], [41] and SVM algorithms [40], [91]. For instance, a combination approach of two machine learning algorithms (e.g., CNN + SVM) has been used to identify the cultivated paddy regions (e.g., Using CNN), and to detect areas damaged (e.g., Using SVM) by Brown Planthopper attacks [40].…”

Section: ) Monitoring Paddy Rice Diseasementioning

confidence: 99%

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Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

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