Browsing by Author "Rajapaksha, RMIK"

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Green insight: a novel approach to detecting and classifying macro nutrient deficiencies in paddy leaves.
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Rathnayake, DMGD; Kumarasinghe, KMSJ; Rajapaksha, RMIK; Katuwawala, NKAC; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Macro nutrient deficiency in paddy leaves is a critical concern in agriculture that impacts crop yield, food security, and sustainable farming. Addressing nutrient deficiencies in paddy plants is vital for ensuring these concerns. This research focuses on automating the detection and classification of common macro-nutrient deficiencies, specifically Nitrogen (N), Phosphorus (P), and Potassium (K). Utilizing image processing techniques, the study identifies distinct color patterns associated with each deficiency, providing a non-invasive and efficient approach. The analysis involves pixel ratio calculations within defined HSV color ranges and threshold values. A modular workflow encompasses preprocessing, horizontal partitioning, pixel ratio computation, and deficiency classification. The innovative methodology we introduced demonstrates promising outcomes, achieving a 96% accuracy rate in identifying nitrogen deficiency, along with 90% accuracy for phosphorus deficiency and 92% accuracy for potassium deficiency detection. While the methodology showcases promise, certain limitations, such as the requirement for leaf symmetry and single-deficiency identification, are recognized. These findings lay the groundwork for more accurate and automated nutrient deficiency detection, and the future work aims to address the identified limitations and generalize the solution for broader applications in real-world agricultural settings.
item: Conference-Full-text
Riceguardnet: custom cnns for precise bacterial and fungal infection classification
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Katuwawala, NKAC; Kumarasinghe, KMSJ; Rajapaksha, RMIK; Rathnayaka, DMGD; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Rice cultivation is a vital component of many nations’ agricultural landscapes, often relying on traditional knowledge passed down through generations. However, disease identification in rice crops presents challenges, as many diseases are difficult to discern through visual inspection alone. This leads to delayed or inaccurate diagnoses, placing entire plantations at risk and discouraging new entrants to the field. This research addresses the pressing issue of timely and accurate disease identification in rice plants, focusing on three common diseases: Bacterial Leaf Blight, Brown Spot, and Leaf Smut, which are caused by bacteria and fungi. These diseases can proliferate rapidly, making early detection crucial. A custom Convolutional Neural Network (CNN) model was developed and trained using a dataset comprising 16,000 images, with 4,000 images for each disease and a healthy class. The model achieved an impressive accuracy of 99.87% on the test dataset, demonstrating its effectiveness in disease classification. This innovative approach provides a solution to the challenges faced by rice farmers, enabling quick and accurate disease identification. The research findings hold significant promise for improving rice cultivation practices, reducing the risk of crop loss, and encouraging new entrants into the field of rice farming.