Agricultural and Biological Engineering

Classification of Impurity Levels in Contaminated Sugarcane Leaf Pellets using NIR Spectroscopy

Thanaporn Saksuwan, Waraporn Sawangchom, Jetsada Posom, Kanvisit Maraphum — Wed, 01 Jul 2026 00:00:00 +0700

This research investigates the feasibility of classifying impurity levels in sugarcane leaf pellets. Samples were prepared by mixing sugarcane leaves with varying proportions of sand. NIR spectroscopy combined with linear discriminant analysis (LDA) was employed to categorize samples across discrete contamination tiers (5% to 25% sand content). Because an absolute 0% clean baseline was not evaluated, the framework is interpreted strictly as a system for grading impurity severity levels. Experimental results revealed that centering and 1^st derivative preprocessing yielded the highest accuracy. These techniques effectively mitigated interference from light scattering and baseline shifts, while enhancing specific spectral features related to biomass chemical constituents, such as cellulose, lignin, and hemicellulose. The LDA model, optimized with preprocessing, achieved classification accuracy exceeding 97%, with minimal errors observed in samples containing low to moderate sand content. However, accuracy slightly declined when sand content exceeded 20% due to reflective interference from silica (SiO₂). The findings confirm that selecting appropriate preprocessing methods is a critical factor for model performance. These laboratory-scale results demonstrate the strong potential of the method, serving as a foundational step toward developing future on-line monitoring pipelines for real-time soil contamination tracking.

Development of a mung bean seeder for sugarcane furrows

Wed, 01 Jul 2026 00:00:00 +0700

This project aims to develop a mung bean seeder for planting in sugarcane rows. The machine has a height of 70 cm and a width of 50 cm. Its main components include a mung bean seed hopper, a screw-type seeding unit, a control system powered by a 12-volt electric motor, and a mounting base. The testing and evaluation were conducted to determine suitable operating conditions and to assess the machine’s performance. The tests were carried out at three travel speeds: 2.60, 3.39 and 4.63 km/h, and three seeding unit rotational speeds: 30, 40 and 50 rpm. The results showed that using gear M3 at a travel speed of 4.63 km/h provided the highest field capacity. Increasing the rotational speed of the seeding unit from 30 to 50 rpm resulted in a decrease in both the seeding rate and uniformity, while the fuel consumption rate decreased slightly. In addition, field tests in actual sugarcane plots indicated that the machine’s actual performance was close to the theoretical values, with a field efficiency of 95.72% and an average fuel consumption rate of 2.14 l/h. These results demonstrate that the machine is suitable and efficient for use under real sugarcane field conditions.

Design of a day–night simulation system using Dynamic Spectrum Lighting (DSL) integrated with artificial intelligence–based automation for regulating the growth of broccoli sprouts

Wed, 22 Jul 2026 00:00:00 +0700

This study aimed to design and evaluate a day–night simulation system using Dynamic Spectrum Lighting (DSL) integrated with an artificial intelligence–based automation control to regulate the growth of broccoli sprouts. A factorial experiment was conducted under photoperiods of 12:12 h, 16:8 h, and 18:6 h combined with blue:red light ratios of 40:60 and 25:75. The results indicated that extending the photoperiod significantly increased the average height from 6.8 ± 0.5 to 11.3 ± 1.1 cm and the fresh weight from 92.4 ± 6.1 to 158.9 ± 12.4 g/tray, while the germination rate ranged from 91.2 to 96.8%. The blue:red light ratio of 25:75 consistently produced superior growth performance compared with the 40:60 ratio. The developed system effectively regulated sprout growth and demonstrates strong potential for application in smart agriculture systems.