Early Detection of Plant Disease Infection Using Hyperspectral Data and Machine Learning

Azmi, Aiman Nabilah Noor; Khairunniza-Bejo, Siti; Jahari, Mahirah; Yule, Ian

doi:10.1007/978-981-19-8113-5_21

Cited by 2 publications

(1 citation statement)

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“…Disease identification and classification dependent upon human judgment always leave errors (Azmi et al, 2023). Moreover, visually observing characteristics is a tedious and timeconsuming job (Petchiammal et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Resistant Source Detection Against Stalk Rot Disease of Maize Using Deep Learning Technique

QURESHI

2023

SABRAOJBG

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Maize incurs many diseases, but stalk rot has badly influenced the crop yield. A pathologist, extension worker, or experienced farmer can only identify susceptible stalks to determine the accurate application of fungicide to the crop. It is rigorous for the farmers of developing countries to hire them in time. Moreover, a variation in the views of professionals leads to incorrect findings. In this manuscript, pathologists’ discoveries have become a standard to compare the farmer’s detections with an intelligent-based model. The Convolutional Neural Network (CNN) employment sought to identify the resistant and susceptible stalk against stalk rot. The Maize and Millet Research Institute Yousafwala, Sahiwal, was the chosen field for experimentation. Gathering resistant and vulnerable images from maize germplasm, having local origins, progressed via a smartphone. The CNN architecture’s exploration classified the images into two resistant and susceptible classes. The P value (0.00001) calculated by the Chi-square method for resistant and predisposed groups showed highly significant results. An 83.88% achieved accuracy came from the CNN, while 49.5% of the accuracy resulted from the farmer. Recording recall ratio and precision of 0.766 and 0.896 occurred for resistant, and 0.911 and 0.796 were the recordings for susceptible classes by deep learning technique, respectively. The proposed approach is an influential source of detection of resistant lines against stalk rot disease by minimizing the need for pathologists, extension workers, or experienced farmers. It will help farmers to identify the quantity of fungicide against stalk rot and explore lines for resistant breeding programs.

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Section: Introductionmentioning

confidence: 99%

Intelligent Resistant Source Detection Against Stalk Rot Disease of Maize Using Deep Learning Technique

QURESHI

2023

SABRAOJBG

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An In-Depth Study of Phytopathogenic Ganoderma: Pathogenicity, Advanced Detection Techniques, Control Strategies, and Sustainable Management

Karunarathna,

Patabendige,

et al. 2024

JoF

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Phytopathogenic Ganoderma species pose a significant threat to global plant health, resulting in estimated annual economic losses exceeding USD (US Dollars) 68 billion in the agriculture and forestry sectors worldwide. To combat this pervasive menace effectively, a comprehensive understanding of the biology, ecology, and plant infection mechanisms of these pathogens is imperative. This comprehensive review critically examines various aspects of Ganoderma spp., including their intricate life cycle, their disease mechanisms, and the multifaceted environmental factors influencing their spread. Recent studies have quantified the economic impact of Ganoderma infections, revealing staggering yield losses ranging from 20% to 80% across various crops. In particular, oil palm plantations suffer devastating losses, with an estimated annual reduction in yield exceeding 50 million metric tons. Moreover, this review elucidates the dynamic interactions between Ganoderma and host plants, delineating the pathogen’s colonization strategies and its elicitation of intricate plant defense responses. This comprehensive analysis underscores the imperative for adopting an integrated approach to Ganoderma disease management. By synergistically harnessing cultural practices, biological control, and chemical treatments and by deploying resistant plant varieties, substantial strides can be made in mitigating Ganoderma infestations. Furthermore, a collaborative effort involving scientists, breeders, and growers is paramount in the development and implementation of sustainable strategies against this pernicious plant pathogen. Through rigorous scientific inquiry and evidence-based practices, we can strive towards safeguarding global plant health and mitigating the dire economic consequences inflicted by Ganoderma infections.

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Early Detection of Plant Disease Infection Using Hyperspectral Data and Machine Learning

Cited by 2 publications

References 50 publications

Intelligent Resistant Source Detection Against Stalk Rot Disease of Maize Using Deep Learning Technique

Intelligent Resistant Source Detection Against Stalk Rot Disease of Maize Using Deep Learning Technique

An In-Depth Study of Phytopathogenic Ganoderma: Pathogenicity, Advanced Detection Techniques, Control Strategies, and Sustainable Management

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