Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R² values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

Sweet potatoes ( Ipomoea batatas L.) are a nutrient‐rich root crop with substantial nutritional, economic, and environmental benefits. This study assessed nine sweet potato genotypes across three distinct locations in Thailand to evaluate yield potential, adaptability, and environmental influences. Trials used a randomized complete block design with four replications. Location 1 (Thatum District) featured sandy soil with moderate pH, high salinity, and balanced nutrients but lower phosphorus (P) and nitrogen (N). Location 2 (Maung District) had sandy, acidic soil but was the most chemically fertile, with high P and potassium (K). Location 3 (Rajamangala University of Technology Isan, Surin Campus) had loamy sand with higher silt content, acidic pH, low P, K, and electrical conductivity (EC). Results highlighted significant yield differences influenced by soil and environmental variability. Genotype SR18003 achieved the highest yield and harvest index, demonstrating strong adaptability and commercial potential. Strong correlations between storage root traits (number, size, and weight) indicate their usefulness for selection. The study underscores the importance of tailored soil management and location‐specific genotypes in optimizing sweet potato production, contributing to resilient varieties, sustainable farming, and improved food security. Further research on genetics and agronomy can enhance breeding and cultivation systems.

Using natural fibers helps reduce the use of fibers separated from environmentally harmful materials. Also, natural fibers reduce agricultural waste and add uniqueness to the fibers. Therefore, the objective of this research was to develop the fiber separation machine and test the efficiency of hemp fiber separation as a tool for peeling hemp fiber. The fiber separation machine consists of a beating blade assembly, a feed chute, a power transmission unit, and a waste discharge chute for the leftover material after fiber extraction. Test factors are as follows: preparation of hemp stalks (mashed stem and no mashed stem), water absorption (6, 12 hr), blade angle (0°, 15°), a set of feeds (1, 2, 3 stems), and speeds of blade (300, 400, 500 rpm). The developed hemp fiber separator effectively separates fibers from hemp stalks. Optimal performance was achieved by pre-crushing the stalks, soaking them for 6 hours, and using a blade angle of 0° at 400 rpm. With a feed rate of two stems, the machine achieved a capacity of 5.52 kg/hr, a separation efficiency of 98.20%, a post-separation loss of 0.98%, and a fiber strength of 3008.83 cN. In the operation of the machine, it consumes 0.40 kW/hr. The payback period for the hemp fiber separator is approximately 1.5 months. The fibers obtained are standardized and can provide more options for textile products, allowing communities to achieve sustainable self-reliance.

This study aimed to examine the effect of different plowing patterns on land preparation efficiency. Three plowing patterns were tested: left-handed plowing, right-handed plowing, and headland plowing. A 90-horsepower tractor fitted with a 7-disc harrow was utilized for the experiments. The analysis of soil properties across all nine plots revealed no significant differences. The average soil moisture content ranged from 15% to 18% (dry basis), while the bulk density of the soil was measured between 1.18 and 1.23 g/cm³. Puncture resistance increased with greater depth. After plowing, the physical properties of the soil, including the average width of the plow furrow and soil depth, were approximately equal, with the width measuring around 127 cm. The depth varied slightly, but both the left-handed and right-handed plowing patterns achieved similar and greater depths compared to the headland plowing pattern. Due to the nature of skip-row plowing, it was necessary to lift and lower the plowshare when beginning a new row. The efficiency of the three methods of land preparation was assessed, with the left-handed plowing pattern demonstrating the highest efficiency, followed by the right-handed pattern, and then the skip-row pattern. The actual work capacities were measured at 2.57, 2.44, and 2.23 rai/hr respectively, while the spatial work efficiencies were 43.34%, 41.60%, and 38.20%. Furthermore, the fuel consumption rates for the three tillage methods did not show significant statistical differences (p > 0.05). The left-handed plowing method exhibited the lowest fuel consumption rate, while the skip-row plowing method provided the most consistent working pattern across the field.