Harumanis demands delicate handling and farmers must know how to use safe pesticides. Therefore, the aim of this study is to examine factors influencing the use of safe pesticide among Harumanis farmers in Mata Ayer, Perlis. The Theory of Planned Behavior (TPB) was used to underpin this study. This study suggests that attitude, subjective norm, perceived behavioral control, knowledge and moral norm as the variables that influence the use of safe pesticides. Data were collected from 97 registered Harumanis farmers under Department of Agriculture (DOA) Malaysia through questionnaires. SmartPLS was used to test the hypotheses in this study. The results showed that attitude, moral norm and knowledge had positive effects on the intention to use safe pesticides. Moreover, subjective norm and perceived behavioral control exhibited insignificant effect on the dependent variable. The results provide helpful information to the stakeholders in the Harumanis industry, from which they could develop and improve appropriate strategies and provide essential assistance to the Harumanis farmers.
Pixel misclassification is a common problem when satellite imagery extracts land-use and land cover classes. Accurate image classification for mangrove areas is essential for management and monitoring to preserve the mangrove ecosystem and expedite the mangrove area delineation process. Therefore, this study aims to i) identify suitable segmentation parameters value to delineate the mangrove area and ii) classify young and mature mangrove trees using the object-based classification (OBIA) approach at Tuba Island, Langkawi, Malaysia. This research applied Support Vector Machine (SVM) based on an object-based method using Sentinel-2A image and segmentation parameters value of scale, compactness, shape, and Gray Level Co-occurrence Matrix (GLCM) mean were tested. Measured tree diameter at breast height (DBH) is used to verify the mangrove tree delineated on the Sentinel-2A image. Segmentation parameters setting of shape (0.2), compactness (0.2), and scale (50) shows minimum errors with mangrove delineation 9.279% as compared to the Global Forest Watch (GFW) data while GLCM mean appropriate to determine the young and mature mangrove tree. The finding of this study will help the Department of Fisheries Malaysia and agritourism to maintain the mangrove ecosystem and enhance the fisheries industry.
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