Image Processing Based Approach for Diseases Detection and Diagnosis on Cotton Plant Leaf

Khairnar, Khushal; Goje, Nitin

doi:10.1007/978-3-030-16848-3_6

Cited by 14 publications

(1 citation statement)

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“…Cotton leaf disease detection is vital for preserving the world's cotton crop, which is essential to the textile sector and the economies of many nations [5]. To prevent extensive crop losses, early and accurate detection of diseases such Fusarium wilt, bacterial blight, and Cotton leaf curl virus is crucial for the implementation of prompt and focused control measures [6].…”

Section: Introductionmentioning

confidence: 99%

Designing a Smart Agri-Crop Framework on Cotton Production using ABO Optimized Vision Transformer Model

et al. 2024

JMC

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Due to its widespread cultivation and large yields by most farmers, cotton is another vital cash crop. However, a number of illnesses lower the quantity and quality of cotton harvests, which causes a large loss in output. Early diagnosis detection of these illnesses is essential. This study employs a thorough methodology to solve the crucial job of cotton leaf disease identification by utilising the "Cotton-Leaf-Infection" dataset. Preprocessing is the first step, in which noise is removed from the dataset using a Prewitt filter, which improves the signal-to-noise ratio. Next, a state-of-the-art process for image classification errands called Vision Transformer (ViT) model is used to carry out the disease categorization. Additionally, the study presents the African Buffalo Optimisation (ABO) method, which optimises weight during the classification procedure. The African buffalo's cooperative behaviour served as the model's inspiration for the ABO algorithm, which is remarkably effective at optimising the model's parameters. By integrating ABO, the problems caused by the dynamic character of real-world agricultural datasets are addressed and improved model resilience and generalisation are facilitated. The suggested ViT-based categorization model shows remarkable effectiveness, with a remarkable 99.3% accuracy rate. This performance is higher than current models.

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Section: Introductionmentioning

confidence: 99%