Jurnal Keteknikan Pertanian

Rapid Prediction of Moisture and Ash Content in Sungkai Leaves Herbal Tea (Peronema canescens Jack.) using NIR Spectroscopy

2024-10-28T14:26:13+07:00

It is imperative to measure the chemical composition of Sungkai leaf herbal tea in order to produce high-quality goods that promote human health. The moisture and ash content of Sungkai leaf herbal tea are critical parameters for assessing the quality of herbal tea. This study aimed to evaluate an NIR spectroscopy method for quickly determining the moisture and ash content of Sungkai leaf herbal tea. Sungkai leaf herbal tea has a moisture content between 3.93% and 7.59%, and an ash content between 3.94% and 5.51%. We developed a calibration model using partial least squares (PLS) with several pretreatment methods. We split the data into calibration and prediction sets and performed an internal random cross-validation. A PLS calibration model with R_p² = 0.86, a root means square error of prediction (RMSEP) of 0.30 (%), and a residual predictive deviation (RPD) of 2.76, performed exceptionally well at predicting the moisture content when the standard normal variate (SNV) pre-treatment was applied to the NIR spectra. The Savitzky-Golay derivative (a 9-point smoothing window, second-order polynomial, dg2) pre-treatment method also generated the best PLS calibration model for ash content determination, with R_p² = 0.70, RMSEP = 0.16 (%), and RPD = 1.86. NIR spectroscopy can quickly determine the moisture and ash content of Sungkai leaf herbal tea, as suggested by these findings.

Analysis of the Supply Chain and Design of an Android-Based Robusta Coffee Traceability System

2024-10-29T08:45:15+07:00

Robusta coffee is the most widely cultivated coffee commodity in Indonesia. in East Bolaang Mongondow Regency, the problem at the suplier level is limited market information, which makes the quality of the coffee beans produced inconsistent, which impacts the price and product quality image. The research aims to identify models and members of structure, added value, performance efficiency, and a traceability system to support product quality validation. Robusta coffee supply chain participants and their interactions was identified at the Modayag coffee plantation in East Bolaang Mongondow. Data was collected through observation, interviews, and field studies. Supply chain analysis was performed using the Vorst method, while added value was calculated using the Hayami method, and supply chain performance was assessed using the SCOR-AHP method. The traceability system design followed the SDLC method. The findings indicate that the Robusta coffee supply chain in East Bolaang Mongondow Regency includes farmers, collectors, processing plants, the retail industry, coffee roatery and consumers. Post-harvest handling involves harvesting, sorting, drying, peeling dry coffee skin, and roasting. The results of the added value show that farmers receive more profits when selling mixed coffee beans that have been processed into dry coffee (greenbeans) 67.1%, the profit ratio for collectors is 20.7%, Kopine Isco 26.3%, D&L coffee 84.5% and Robusta Gunung Ambang 63.9%. The supply chain performance measurements showed 51.2% at the farmer level, 57.7% for collectors, and 63.8% for processing plants. A traceability system named Kinton was successfully developed and integrated with the Firebase database to store all information.

The Development Mask R-CNN Model for Identification of Melon Plant Leaves and Branches

2024-10-28T15:57:22+07:00

The quality of melons can be enhanced and optimized by pruning melon plants. Pruning is a removal process carried out on specific parts of the plant. Currently, melon plants are still pruned manually by farmers, but there are many drawbacks to this method. In this research, pruning is conducted on the branches and leaves of melon plants. Pruning can be facilitated with the assistance of a robot capable of recognizing leaves and branches. In this study, the method used to detect branches and leaves is the Mask Region-based Convolutional Neural Network (Mask R-CNN). Hyperparameter tuning technique is employed to obtain the best parameter values, including learning rate, weight decay, and learning momentum. Two scenarios are considered in this research, one with 10 epochs and the other with 30 epochs. The obtained Average Precision (AP) values at 10 epochs are 32.2% for leaf objects and 0% for branches. At 30 epochs, the AP values are 56.8% for leaf objects and 4.1% for branches. The mean Average Precision (mAP) is 16.1% for 10 epochs and 28.4% for 30 epochs.

Pesticide Residue Reduction on Curly Chili (Capsicum annum L.) Using Ozone Fine Bubble Technology.

2024-10-30T08:48:34+07:00

Pesticide residues in curly chilies may cause health problems in consumers. Washing curly chilies using ozone fine-bubble water is a promising method for reducing pesticide residues. The aim of this study was to determine the optimal dose and duration to degrade pesticide residues, especially for profenofos, and to determine their effect on the shelf life and physical quality of curly chilies. After washing, the curly chilies were stored at room temperature and observed every two days. The results showed that washing with 1 ppm ozone fine bubble water for 10 min was effective in reducing profenofos residue on curly chili by up to 89.8% without reducing its quality. The shelf life of curly chilies was observed, and they started losing their commercial value after 6-8 days.