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p-Index From 2021 - 2026
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All Journal Journal Of World Science Journal of Agricultural Economy and Technology Development
Millatul Maula, Indi
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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Soil Degradation and its Challenges to Agricultural Production Resilience: an Overview from a Soil Traits Perspective Millatul Maula, Indi
Journal of World Science Vol. 5 No. 1 (2026): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v5i1.1620

Abstract

Soil degradation is a major problem that threatens the resilience of agricultural production due to the decline in the soil's ability to support sustainable plant growth. This study aims to examine soil degradation and its challenges to agricultural production resilience from the perspective of soil characteristics. The method used is literature review with a narrative review approach to previous research scientific articles. The literature analyzed is publications from the last ten years relevant to soil degradation, soil physical and chemical properties, and agricultural productivity. The results of the study show that the degradation of soil physical properties, such as compaction, structural damage, and erosion, as well as the degradation of soil chemical properties, especially the decline of organic matter and nutrient imbalances, have a significant effect on the decline and instability of agricultural production. The literature synthesis confirms that soil degradation is a strategic challenge in maintaining the long-term resilience of agricultural production. Therefore, a soil quality-based approach is needed to support the sustainability of agricultural systems.
Machine Learning-Based Disease Detection in Cocoa Plantations: Economic Viability Study in Luwu Regency, South Sulawesi Millatul Maula, Indi
Journal of Agricultural Economy and Technology Development Vol. 2 No. 2 (2025): Journal of Agricultural Economy and Technology Development
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jaetd.v2i2.42

Abstract

Disease-related yield losses represent a critical constraint to cocoa productivity in Indonesia, particularly in South Sulawesi Province where endemic infections cause 30-70% production declines annually. This study evaluated the economic viability of implementing machine learning-based disease detection systems in cocoa plantations in Luwu Regency through an 18-month mixed-methods research design integrating technical validation, randomized controlled field trials, and comprehensive economic analysis. A convolutional neural network model was developed using 12,450 labeled images and deployed across 30 cocoa farms stratified by size and disease pressure, with 15 treatment farms receiving ML-based detection technology and 15 control farms continuing conventional monitoring practices. The ML model achieved 93.7% diagnostic accuracy for detecting Cocoa Pod Borer, Vascular Streak Dieback, and Black Pod Disease. Treatment farms demonstrated significantly higher yields (1,247 kg/ha vs. 942 kg/ha, 32.4% increase), reduced disease incidence (8.7% vs. 23.1%), and improved bean quality (73.2% Grade A vs. 58.4%). Economic analysis revealed highly favorable investment returns with Internal Rate of Return of 47.3% for individual adoption and 52.6% for cooperative models, Net Present Value of $2,847 per farm, Benefit-Cost Ratio of 3.68, and Payback Period of 2.8 years. The findings demonstrate that ML-based disease detection achieves economic viability in smallholder cocoa farming contexts, offering a transformative solution for enhancing agricultural productivity and farmer incomes in disease-endemic tropical plantation systems.  
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