With the surge in digital farming, real-time quality management of fresh produce has become essential. For apples (Malus domestica Borkh.), consumer demand extends beyond sweetness, texture, and appearance to internal quality factors such as moisture content. Existing non-destructive methods, however, involve costly equipment, complex calibration, and sensitivity to environmental conditions. This study hypothesizes that thermal diffusivity indices derived from surface heating and cooling patterns can accurately predict apple moisture content non-destructively. A total of 823 apples from seven varieties were analyzed using a thermal imaging sensor in a 120-s process comprising 40 s of heating and 80 s of cooling. Key thermal diffusivity indices—minimum, maximum, mean, and max–min values—were extracted and correlated with actual moisture content measured via the drying method. Multiple linear regression and leave-one-out cross-validation confirmed that mean temperature-based models provided the most stable predictions ($R_{CV}^2$ ≥ 0.90 for some varieties). Frame optimization and artificial neural networks further improved prediction accuracy for varieties exhibiting higher variability. The proposed method is cost-effective, requires minimal calibration, and is less affected by surface reflectance, outperforming conventional optical methods (e.g., NIR spectroscopy, hyperspectral imaging), especially regarding robustness against surface reflectance variability and calibration complexity. This offers a practical solution for monitoring apple freshness and quality during sorting and distribution processes, with expanded research on sugar content and acidity expected to accelerate commercialization.

Fossil fuels are associated with problems such as resource depletion and pollution, necessitating the exploration of alternatives. Giant miscanthus (Miscanthus × giganteus Greef et Deu), a perennial that can be harvested yearly, requires a low production energy input. It has less ash content and high heat efficiency and has attracted attention as an energy source. An on-site processing equipment, powered via a tractor and equipped with a chipper and a two-stage compression roller, was developed that can harvest 1000 kg of giant miscanthus per hour and simultaneously produce compressed pellets eliminating unnecessary processes such as transportation and processing. With its use, 33–74.5 kWh/t of electrical energy can be saved by producing pellets. The changes in moisture content between the produced compressed pellets and two samples of the ground product were measured immediately before compression for 24 h at relative humidity ranging from 65% to 80%. The moisture content was 6% initially; it ranged from 6.71% to 7.81% in compressed pellets, depending on the conditions, and from 7.44% to 9.82% in the ground sample immediately before compression, indicating the effect of the physical form of the biomass and humidity in the environment. The possible storage period (while maintaining the moisture content at 8–10% for optimal biofuel efficiency based on the measured data) was predicted. The optimal relative humidity of the storage environment for maintaining biomass quality for more than 6 months was predicted to be ≤77% and ≤70% for the compressed pellet and ground sample, respectively. Moreover, at a relative humidity ≥77%, giant miscanthus biomass, immediately before compression, had >10% moisture content in 2 days, warranting caution in storage.

Accurate analyses and management of ion concentrations are crucial in precision agriculture. Modern technology-based methods are non-destructive and do not require sample preparation, enabling fast and accurate analysis; however, they have limitations when processing multiple samples. In this study, a multi-ion analysis system was developed for the prompt and accurate analysis of concentrations of important ions such as NO₃⁻, H_nPO₄⁻, K⁺, Ca²⁺, and Mg²⁺. The RGB sensitivity control was automated through calibrations by applying a reference slot-based error rate across six slots between sample measurements, facilitating sample-to-sample comparisons and enabling accurate concentration analysis. By analyzing the correlation between each ion concentration and the proximal absorbance-based concentration prediction in the simultaneous analysis system, the accuracy was verified by achieving a coefficient of determination exceeding 0.99 for most ions. This system minimizes possible deviations between slots by using an automatic calibration algorithm, thereby facilitating the simultaneous analysis of multiple samples. This is important for saving time and cost and can help in real-time nutrient analysis and monitoring in agriculture.