The development of non-destructive methods for early detection of cold stress of plants and the identification of cold-tolerant cultivars is highly needed in crop breeding programs. Current methods are either destructive, time-consuming or imprecise. In this study, soybean leaves’ spectra were acquired in the near infrared (NIR) range (588–1025 nm) from five cultivars genetically engineered to have different levels of cold stress tolerance. The spectra were acquired at the optimal growing temperature 27 °C and when the temperature was decreased to 22 °C. In this paper, we report the results of the aquaphotomics analysis performed with the objective of understanding the role of the water molecular system in the early cold stress response of all cultivars. The raw spectra and the results of Principal Component Analysis, Soft Independent Modeling of Class Analogies and aquagrams showed consistent evidence of huge differences in the NIR spectral profiles of all cultivars under normal and mild cold stress conditions. The SIMCA discrimination between the plants before and after stress was achieved with 100% accuracy. The interpretation of spectral patterns before and after cold stress revealed major changes in the water molecular structure of the soybean leaves, altered carbohydrate and oxidative metabolism. Specific water molecular structures in the leaves of soybean cultivars were found to be highly sensitive to the temperature, showing their crucial role in the cold stress response. The results also indicated the existence of differences in the cold stress response of different cultivars, which will be a topic of further research.

Agarwood is a unique and valuable resin synthesized within a few tree species of the tree family Thymelaeaceae primarily as a self-defence mechanism against stress causes such as fungal invasions. Instead, artificial inoculation leads to short-time harvesting practiced in intensive commercial-level cultivations. The extraction of this resin can be done only after the harvesting where accurate estimation of resin yield is crucial for avoiding immature or underdeveloped harvesting. However, the amount of resin accumulated in the trunk cannot be estimated by the outside inspection. As a result, trees are being cut down before they have reached their maximum harvest potential. Therefore, precise detection of fragrant resin formation status inside the tree has become one of the primary challenges in the agarwood production industry. Currently, the methods available are either destructive, inefficient, or inaccurate. As such, this study evaluates the possibility of the use of NIR spectroscopy as a non-destructive and quick detection method for agarwood formations status inside trees in the in-situ mode. Near-infrared (NIR) range (588–1,025 nm) spectra were acquired from resin-formed and non-formed areas from twenty tree trunks of agarwood with five different formation levels. The best agarwood formation areas of logs were assigned as class level 1 and the least was assigned as the class level 5. The spectra were acquired near the opening made for the artificial inoculation process of considered Aquilaria crassna tree trunks. The results of Soft Independent Modelling of Class Analogies showed consistent evidence of differences in the NIR spectral profiles of all levels of agarwood formations. The SIMCA discrimination between the resin developed versus not was achieved with 86% accuracy. The accuracy rates of the degree of the presence of agarwood were obtained as 80%, 100%, 85%, 90%, and 85% respectively, as assigned levels 1 to 5 in the SIMCA models. This research revealed the potential possibility of using NIR technology for the prediction of agarwood resin status inside the agarwood tree in A. crassna before harvesting the tree as a non-destructive and rapid method. Keywords: NIR spectroscopy, Agarwood, Aquilaria crassna, Non-destructive, Agarwood formation

Agarwood is a bio-resin that withstands a consistent market demand worldwide due to its exceptional formula of fragrant resin and rare essence of herbal aromatic compounds. However, the Agar-resin is formed only in the Thymalaeaceae family trees as a self-defense mechanism towards plant stress. In commercial-level agarwood production, artificial inoculation is essential for reliable agarwood production. However, the prevailing method of agar-based inoculum preparation has been plagued by the tedious, time-consuming, and poor texture of insufficient blending via particle blockages in the field operation is frequent. As such, this study invented a novel Agarwood-Induce Inoculum Mixing Machine addressing the shortcomings of the prevailing hand-held type mixing machine. The operation performance of both machines was systematically evaluated for the time required for a single bend, electrical energy consumption, and the quality of the final product. The results revealed a drastic reduction of time required for the completion single blend by 6 times i.e. from 18 minutes to 3 minutes by using the newly invented machine. The total electric energy savings for the completion of one batch of the blend was found to be 2.4 times (energy reduction from 0.48kWh to 0.2kWh) than the existing method. In addition to the two sample T-test results, a substantial reduction of time and energy with p-values close to zero (p<0.001), an exceptional quality homogeneous creamy foam of the final blend which is vital for a successful inoculation was received. The quality of the produced inoculum was rigorously assessed by a custom-formulated five-parameter index and 96.25% of the sample from the new machine and ranked “excellent" while the existing hand-mixing machine was with diverse performance. According to the chi-square test, there was a statistically significant difference (p<0.001) in the quality of the output inoculum between the two methods. The final blend of inoculum produced by the new machine was found to have exceptional quality homogeneous creamy foam texture which is vital for providing a hassle-free convenient operation background of the fields during the inoculation process.

Rice production systems and breeding programmes often require handling large numbers of paddy samples, where maintaining sample identity is critical. Conventional identification methods, such as morphological and molecular analyses, are reliable but laborious and time-consuming. To address this, the present study evaluated the use of near infrared (NIR) spectroscopy (588–1091 nm) combined with soft independent modeling of class analogy (SIMCA) for rapid discrimination of paddy seed varieties. A total of 750 seed samples representing five varieties (Bg300, Bg352, Bg357, Bg359, and Bw363) were scanned, and SIMCA prediction models were developed using Pirouette 3.11 software. Model parameters were optimized to enhance classification performance. The influence of moisture content (MC) at four levels (10%, 12%, 15%, and 23%) during seed handling was also investigated. The SIMCA models achieved variety-specific classification accuracies of 89% (Bg300), 83% (Bg352), 88% (Bg357), 87% (Bg359), and 84% (Bw363). When averaged across all varieties, correct identification rates were 82% at 10% MC, 83% at 12% MC, 88% at 15% MC, and 92% at 23% MC. The results confirm an increasing trend in predictive accuracy with elevated MC, attributable to stronger variety-specific water-related absorbance features in the NIR region. Overall, the study demonstrates that NIR spectroscopy combined with SIMCA provides a rapid, non-destructive, and feasible approach for varietal identification of paddy seeds, particularly effective under equalized moisture levels. This method holds strong potential for field-level application, offering faster and more reliable variety authentication compared to conventional practices.