This review aimed to examine the application potential of triboelectric nanogenerators (TENGs) in agricultural micro-energy (ME) harvesting and self-powered sensing. An emphasis was focused on whether weak, low-frequency, and distributed energy sources under agricultural environments were converted into usable electrical energy, and whether TENG-based devices were supported battery-free monitoring of key environmental and biochemical parameters. Technical challenges were also identified using field deployment. A summary was finally given for the directions on TENG energy harvesting and sensing in smart agriculture. A comparison was made on contact electrification and electrostatic induction, in terms of device structures, materials, and hybrid configurations. ME harvesting was classified into wind, raindrops, and water flow, whereas self-powered sensing was for wind speed, wind direction, flow behavior, and nutrient concentration. A systematic evaluation was conducted on output and sensing performance, stability, as well as system integration. The results showed that TENGs provided an effective technical route to collect irregular agricultural energy and then convert mechanical interaction into electrical signals. Low-speed startup, airflow adaptability, and output stability were improved in wind energy harvesting using rotary devices with swing and blade structure in adaptive direction. Direction-adaptive hybrid devices exhibited stable output under variable wind conditions and were capable of powering multiple wireless sensors, indicating their adaptability to open farmland and greenhouse ventilation environments. For raindrop and water-flow energy harvesting, liquid-solid TENGs, droplet-based generators, water-tube structures, and waterwheel hybrid systems have been developed to utilize rainfall, irrigation flow, and dynamic liquid-solid interfaces. Their performance was improved after surface modification, hydrophobic materials, optimal electrode structures, and coupling with electromagnetic generators. Electrical output was used to support low-power sensing, wireless transmission, and liquid-state monitoring. In self-powered sensing, TENGs directly converted external mechanical or liquid disturbances into measurable electrical signals. Reported sensing targets included wind speed, wind direction, liquid flow rate, droplet behavior, leakage, blockage, and nutrient concentration. Wind sensors were used to monitor airflow in the range of 3-5 m/s, while biochemical sensing devices were to detect urea concentrations from 0-5 m/L. Microfluidic and droplet-based devices also showed sensitivity to bubble state, liquid motion, bacterial targets, and chemical composition. Therefore, TENG-based sensors shared the simple structure, active signal generation, low power demand, and compatibility with distributed monitoring nodes. Their output was still dominated by humidity, surface contamination, material wear, environmental disturbance, and load matching. Key challenges remained on output fluctuation, long-term durability, packaging reliability, and power application. TENGs can be expected for agricultural ME harvesting and self-powered sensing, especially in scenarios where conventional batteries difficult to replace or maintain. Future studies should improve structural robustness, material stability, environmental resistance, and consistent output. TENG devices can also integrate with power circuits, energy storage modules, and low-power wireless communication units. Full sensing is often required to combine energy harvesting, signal processing, data transmission, and field calibration in smart agriculture

Oil content, protein, glucosinolates, and internal qualities are required to be detected during harvesting, transportation, storage, and sale of rapeseed. In this study, a portable device was designed to detect the rapeseed internal quality using near-infrared spectroscopy, in order to realize the multi-index, portable, and rapid non-destructive testing. The hardware was integrated with the dimensions of 246 mm × 128 mm × 127 mm, such as a mini spectrometer, an LCD touchscreen, and a Raspberry Pi processor. The device was operated stably outdoors for 6 hours. The 65 varieties of rapeseed were sampled from different production areas. Diffuse reflectance spectra were then collected from 900 to 1700 nm. The wavelength stability tests were carried out to determine a stable spectral region from 900 to 1 633 nm. Data was divided using KS (Kennard-Stone), SPXY (sample set partitioning based on joint X-Y distances), and Random number. The dataset division was obtained with R² as the index, where the KS was used for the oil content and protein, while the random numbers were for the glucosinolates, erucic acid, and moisture content. Various methods of data smoothing were evaluated with data smoothness as the index, such as SNV, SG, MSC, D1, and D2. SG smoothing (5-window, and 3rd order) was determined as the best preprocessing. Cars, Pca, GA, Lars, Uve, and Spa were used as data reduction to explore the best dimension reduction for each physicochemical index. Cars reduced the dimensions of the oil content model by 73%. Pca reduced the dimensions of the protein and erucic acid models by 98%. Thus the predictive accuracy of erucic acid was improved by 31.49%, whereas, the predictive error was reduced by 40.78%. PLS, ANN, CNN, SVR, and ELM models were used with RMSE, R², and MAE as the indices. The calibration model was determined for the oil content using KS+SG+Cars+PLS, for the protein using SPXY+SG+Pca+PLS, for the glucosinolates using Random+SG+PLS, for the moisture content using Random+SG+ELM, and the for erucic acid using Random+SG+Pca+PLS. Model indices were as follows: RMSE, R², and MAE for the oil content were 1.40%, 0.95, 1.16%; for the protein 1.46%, 0.86, 1.24%; for the glucosinolates 20.70 μmol/g, 0.73, 15.73 μmol/g; for the erucic acid 3.63%, 0.86, 3.28%; for the moisture content 0.36%, 0.98, 0.24%, respectively. The device was used to collect the spectra from 1 to 7 g of rapeseed seven times for the light transmission. Results showed that the absorption error for 3 to 7 g was within ±5%. Electronic scales were then removed for direct sample testing during field tests. Five stability tests were conducted at temperatures from 15 to 35 ℃, all of which were within reliable ranges; Stability tests were set as the relative humidity from 40% to 80% and found condensation at 70% relative humidity, which was corrected to maintain stability from 40% to 70% relative humidity. Accuracy tests on the device showed the correlation coefficients (R²) for oil content, protein content, glucosinolates, moisture content, and erucic acid between predicted and real sets at 0.932, 0.855, 0.734, 0.968, and 0.761, respectively; RMSE values were 1.35, 1.67, 19.6, 0.34, and 2.96, respectively. Therefore, the device can be expected to perform real-time non-destructive testing of oil content, protein, glucosinolates, moisture content, and erucic acid in rapeseed after safe storage.