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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Applied Agricultural Science and Technology
Suputa Suputa
Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT

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Detection of Spodoptera exigua (Hübner) Larval Infestation on Leeks and Shallots: A Case Study in North Tapanuli and Karo, North Sumatra, Indonesia Ameilia Zuliyanti Siregar; Suputa Suputa; Lindung Tri Puspasari; Abdul Hafiz Ab. Majid
Journal of Applied Agricultural Science and Technology Vol. 10 No. 1 (2026): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v10i1.368

Abstract

North Sumatra Province is the eighth largest producer of shallots (Allium cepa) in Indonesia (1.09%, 53,962 tons). Declining shallot production is primarily caused by attacks by pests and pathogens, which are responsible for 20–100% decreases in yields. Spodoptera exigua larvae are among pests with significant effects on shallot productivity. This study aimed to determine the effectiveness of various methods of monitoring Spodoptera exigua infestation on shallots, particularly of the Batu Ijo and Brebes varieties, including the use of yellow sticky traps, blue ball traps, sweep nets, and hand-picking, for the purpose of increasing farmers' income in North Tapanuli and Karo, Sumatra Utara. The research was conducted in Parhorboan Village, Pagaran Sub-district, North Tapanuli Regency, and Juhar Sub-district, Karo Regency, North Sumatra, from June to July 2024. To be precise, it was conducted in three farmers' planting areas, using the diagonal slice method with five sub-plots and observations with one-week intervals at the sampling locations. The results showed that the infestation by S. exigua larvae was higher on shallots than on leeks. The highest average number of larvae recorded on leeks was 0.78 larva per plant, while shallots had an average of 1.84 larvae per plant. Furthermore, the highest average percentage of S. exigua larval infestation on leeks was 16.78%, while the infestation on shallots reached 32.15%. The intensity of S. exigua infestation fell within the 3–5 categories, which correspond to medium to very high levels. The independent sample t-test results showed significant differences in both the population and infestation percentage of S. exigua larvae in the leek and shallot planting areas.
A comparative study of mango fruit pest and disease recognition Kusrini Kusrini; Suputa Suputa; Arief Setyanto; I Made Artha Agastya; Herlambang Priantoro; Sofyan Pariyasto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 6: December 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i6.21783

Abstract

Mango is a popular fruit for local consumption and export commodity. Currently, Indonesian mango export at 37.8 M accounted for 0.115% of world consumption. Pest and disease are the common enemies of mango that degrade the quality of mango yield. Specialized treatment in export destinations such as gamma-ray in Australia, or hot water treatment in Korea, demands pest-free and high-quality products. Artificial intelligence helps to improve mango pest and disease control. This paper compares the deep learning model on mango fruit pests and disease recognition. This research compares Visual Geometry Group 16 (VGG16), residual neural network 50 (ResNet50), InceptionResNet-V2, Inception-V3, and DenseNet architectures to identify pests and diseases on mango fruit. We implement transfer learning, adopt all pre-trained weight parameters from all those architectures, and replace the final layer to adjust the output. All the architectures are re-train and validated using our dataset. The tropical mango dataset is collected and labeled by a subject matter expert. The VGG16 model achieves the top validation and testing accuracy at 89% and 90%, respectively. VGG16 is the shallowest model, with 16 layers; therefore, the model was the smallest size. The testing time is superior to the rest of the experiment at 2 seconds for 130 testing images.
Co-Authors Abdul Hafiz Ab. Majid Ameilia Zuliyanti Siregar Arief Setyanto Herlambang Priantoro I Made Artha Agastya Kusrini Lindung Tri Puspasari Sofyan Pariyasto