Research Publications

Now showing 1 - 3 of 3

Embargo
Bridging tradition and innovation: exploring vegetable harvest loss reduction strategies in Sri Lanka
(Emerald Publishing, 2026-01-15) Jayasuriya, N; Yapa, C.G; Rathnayake, T.A; Dilhara, A; Rathnayake, I.D; Mathangadeera, R
Purpose – This study aims to address a significant gap in the literature regarding vegetable harvest loss reduction methods, exploring both traditional and modern perspectives in Sri Lanka, which is largely driven by an agricultural economy. This study explores the diverse strategies employed and how they are going to be integrated by Sri Lankan vegetable farmers, highlighting both traditional and modern pre- and post-harvest practices aimed at improving productivity, sustainability and resilience in agricultural systems. Design/methodology/approach – The study was conducted across key agricultural districts in Sri Lanka, with data collected through semi-structured interviews with vegetable farmers using the snowball sampling method. Thematic analysis was employed to identify patterns and themes in the data. Findings – The findings emphasize the importance of traditional methods, including cultural practices such as cultivating at auspicious times, established pest control and irrigation techniques. These are complemented by advanced agricultural innovations, modern harvest protection methods and improved packing and transportation techniques. This integrated approach showcases farmers' adaptability in reducing vegetable losses despite the challenges they face. Originality/value – Post- and pre-harvest loss reduction in Asian countries can be considered an understudied area. Furthermore, the focus on traditional methods is rare in the field. Therefore, this study provides a clear understanding of traditional and modern methods that are suitable for farmers in developing countries
Embargo
VAPECA - Smart Agricultural and Analysis Monitoring System
(Institute of Electrical and Electronics Engineers, 2022-10-15) Jithmal Pitigala, P. K. D. U; Laksahan, T. M. K; Hewapathirana, S. S; Sadeepika Herath, H. M. H; Chandrasiri, S; Nadeesa Pemadasa, M. G
Agriculture dramatically contributes to the economy by creating a monetary future for developing nations. However, in Sri Lanka, the farmers have confined resources and encounter numerous challenges to enrich their crop productivity and prevail in the competitive business world. In the directive, the farmers' knowledge about export crops and weak decision- making needs to be exposed [1]. This study has built a mobile application with budget planning, determining plant conditions, weather forecasting, analyzing harvest quality, and a price prediction system to mitigate these hardships. This application would be utilized to manage three critical plants in Sri Lanka t for extraction and export. Those are Vanilla, Pepper, and Cardamom. The key technologies used for the system are deep learning and machine learning. The overall system obtained desirable outcomes with an accuracy rate higherthan 94%-97%. The ultimate intent of this study is to achieve the optimal growth of the agriculture sector by navigating the farmers to get maximum crop yield, quality, and effective decision-making through reliable market trends and to enhance the farmers' profit
Open Access
IOT-based Monitoring System for Oyster Mushroom Farms in Sri Lanka
(KDU IRC, 2022-01-10) Surige, Y. D; Perera, W. S. M; Gunarathna, P. K. M; Ariyarathna, K. P. W; Gamage, N. D. U; Nawinna, D. P
Oyster Mushrooms are a type of a fungus which is very sensitive to the environmental factors and vulnerable to diseases and pest attacks which directly effects local trade and export strength. Mushroom is a climacteric type of food which continues its cycle even after harvesting. The mushroom farming process still uses manual mode such as the identification of diseases uses a farmers eye visually, harvesting of mushrooms are decided based on the visual appearance while the environmental factors are decided based on gut feelings. These methods has its limitations which requires more potential to improve both the quality and capacity of mushroom production. With the advancements of technology, this farming process can be performed with the aid of an IoT device and deep learning model. This research applies Convolutional Neural Networks (CNN) with Mobile Net V2 model to detect mushroom harvest time and any disease spread with an accuracy of 92% and 99% respectively. Long Short-Term memory (LSTM) to analyze the detected environmental factors with an accuracy of 89% and this system predicts the yield of mushroom production with the support of LSTM model with an accuracy of 97%. This developed system which aids mushroom farming activities is connected with the farmers through s mobile application

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