Vegetation indices based segmentation for automatic classification of brown spot and blast diseases of rice

Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

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“…Yield [79]; biomass [28,153]; disease [157]; N-concentration and uptake [142]; water stress [158] Modified soil adjusted vegetation index (MSAVI)…”

Section: Rnir−rgreen Rnir+rgreenmentioning

confidence: 99%

“…2.5(RNIR−R Red ) (RNIR+6R Red −7.5R Blue +1) Disease [157]; biomass [28] Normalized water index (NWI) R970−R900 R970+R900…”

Section: R531−r570 R531+r570mentioning

confidence: 99%

Applications of Remote Sensing in Precision Agriculture: A Review

2020

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“…These sixteen features were used with minimum distance classifier (MDC) and kNN, yielding an accuracy of 87.02% and 89.23% respectively. Phadikar S. and Goswami J. proposed another automated rice disease classification algorithm using vegetation indices based segmentation [103]. Unlike their previous work, the authors focused their work on just two diseases: brown spot and rice blast.…”

Section: Identification Of Rice Diseases Pests and Foreign Particlesmentioning

confidence: 99%

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

Ahmed

Ali²,

Khan

2021

IEEE Access

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“…during their growth and development (Suzuki et al, 2014), resulting in abnormal physiological and morphological changes in plants. In severe cases, it may disrupt its normal growth and development and even cause large-scale disasters, such as leaf spot disease (Ozguven and Adem, 2019), powdery mildew (Lin et al, 2019), brown spot and blast diseases (Phadikar and Goswami, 2016), and gray mold (Fahrentrapp et al, 2019). The prior symptoms of these diseases include leaf discoloration, tissue deformation or necrosis, and root atrophy, etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

et al. 2022

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Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.

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Vegetation indices based segmentation for automatic classification of brown spot and blast diseases of rice

Cited by 35 publications

References 14 publications

Applications of Remote Sensing in Precision Agriculture: A Review

Applications of Remote Sensing in Precision Agriculture: A Review

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

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