Plant diseases cause considerable economic loss in the global agricultural industry. A current challenge in the agricultural industry is the development of reliable methods for detecting plant diseases and plant stress. Existing disease detection methods mainly involve manually and visually assessing crops for visible disease indicators. The rapid development of unmanned aerial vehicles (UAVs) and hyperspectral imaging technology has created a vast potential for plant disease detection. UAV-borne hyperspectral remote sensing (HRS) systems with high spectral, spatial, and temporal resolutions have replaced conventional manual inspection methods because they allow for more accurate cost-effective crop analyses and vegetation characteristics. This paper aims to provide an overview of the literature on HRS for disease detection based on deep learning algorithms. Prior articles were collected using the keywords “hyperspectral”, “deep learning”, “UAV”, and “plant disease”. This paper presents basic knowledge of hyperspectral imaging, using UAVs for aerial surveys, and deep learning-based classifiers. Generalizations about workflow and methods were derived from existing studies to explore the feasibility of conducting such research. Results from existing studies demonstrate that deep learning models are more accurate than traditional machine learning algorithms. Finally, further challenges and limitations regarding this topic are addressed.

This study aimed to investigate the residual characteristics of pesticides drifted by unmanned aerial spray according to buffer strip, windbreak, and morphological characteristics of non-target crops, suggest prevention for drift reduction, and finally conduct a risk analysis on pesticides exceeding the maximum residue limit (MRL) or uniform level (0.01 mg/kg) of the positive list system (PLS). Non-target crops were collected around the aerial sprayed area (paddy rice) in Boryeong, Seocheon, and Pyeongtaek after UAV spray. When pesticides were detected in more than three samples, Duncan’s multiple range test was performed. In cases where pesticides were detected in only two samples, an independent sample t-test was conducted (p < 0.05). The drift rate of pesticides tends to decrease by up to 100% as the buffer distance from aerial sprayed area increases or when a windbreak, such as maize, is present between two locations. Thus, the reduction of drifted pesticides could be effective if both factors were applied near the UAV spray area. Moreover, the residue of drifted pesticides was found to be the highest in leafy vegetables such as perilla leaves or leaf and stem vegetables such as Welsh onion, followed by fruit vegetables and cucurbits, owing to the morphological characteristics of crops. Therefore, selecting pulse or cereal such as soybean or maize as a farm product near the UAV spray area can be considered to minimize the drift. For pesticides that exceed the MRL or PLS uniform level, %acceptable dietary intake is 0–0.81% with no risk. Additionally, employing pesticides approved for both paddy rice and farm products in UAV spraying can effectively minimize instances where MRL or PLS are exceeded. Therefore, this study aims to provide farmers with effective guidelines for mitigating drift. Furthermore, we strive to promote stable and uninterrupted food production while facilitating the utilization of agricultural technologies such as UAV spraying to address labor shortages and ensure sustainable food security.

Since the introduction of the positive list system (PLS) for agricultural products in the Republic of Korea, the demand for a quick, easy multi-residue analysis method increased continuously. Herein, the quick, easy, cheap, effective, rugged, and safe (QuEChERS) technique combined with liquid chromatography–tandem mass spectrometry was employed to optimize a method for the multi-residue analysis of 287 pesticide residues in mandarin orange and grapefruit. Method validation was conducted in terms of selectivity, limit of quantitation (LOQ), linearity, accuracy, precision, and matrix effect. All the compounds at low spiking levels (1, 2.5, 5, or 10 mg/kg) could be quantified at LOQs lower than 0.01 mg/kg (PLS level). The linearity of the matrix-matched calibration curve for each compound is in the range 0.5–50 μg/L, and its coefficient of determination (R²) is >0.990. Satisfactory recovery values of 70–120% with a relative standard deviation of ≤20% are obtained for all compounds in the mandarin orange and grapefruit samples. A negligible matrix effect (−20–20%) is observed for more than 94.8% and 85.4% of the pesticides in mandarin orange and grapefruit, respectively. Therefore, this analytical method can contribute to pesticide residue analyses of citrus fruits for routine laboratory testing.

Tenebrio molitor larvae (mealworm) is an edible insect and is considered a future food. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), a novel method for simultaneous analysis of 353 target analytes was developed and validated. Various sample preparation steps including “quick, easy, cheap, effective, rugged, and safe” (QuEChERS) extraction conditions, number of acetonitrile-hexane partitions, and dispersive-solid phase extraction (dSPE) sorbents were compared, and the optimal conditions were determined. In the established method, 5 g of homogenized mealworms was extracted with acetonitrile and treated with QuEChERS EN 15662 salts. The crude extract was subjected to three rounds of acetonitrile-hexane partitioning, and the acetonitrile layer was cleaned with C18 dSPE. The final solution was matrix-matched and injected into LC-MS/MS (2 μL). For target analytes, the limits of quantitation (LOQs) were ≤10 μg/kg, and the correlation coefficient (r²) of calibration was >0.990. In recovery tests, more than 90% of the pesticides showed an excellent recovery range (70–120%) with relative standard deviation (RSD) ≤20%. For more than 94% of pesticides, a negligible matrix effect (within ±20%) was observed. The analytical method was successfully applied and used for the detection of three urea pesticides in 4 of 11 mealworm samples.

An effective analytical method was optimized for residues including chlorpyrifos–methyl, deltamethrin, fenoxanil, thiobencarb and fludioxonil in mealworms, the larval form of Tenebrio molitor. They are listed for pest control during wheat cultivation and can be found in wheat-bran feed for growing mealworms in South Korea. Analytes were extracted using acetonitrile and salt packet. Four clean-up methods ((1) MgSO₄ + 25 mg PSA + 25 mg C18; (2) MgSO₄ + 50 mg PSA + 50 mg C18; (3) EMR-lipid^TM tube; and (4) 10 mL n-hexane) were investigated and the method (1) was selected due to its robustness. Low-temperature precipitation of fat and proteins improved the recoveries. Recoveries from the Method (1) were satisfying with 70–120% with <20% relative SD at a spiking level of 0.01 mg/kg. With the simultaneous sample preparation, fenoxanil, thiobencarb and fludioxonil were analyzed by liquid chromatography tandem mass spectrometry (LC–MS/MS) and chlorpyrifos–methyl and deltamethrin by gas chromatography tandem mass spectrometry (GC–MS/MS). Quantification limits for LC–MS/MS and GC–MS/MS were 0.5 and 2.5 μg/L, respectively. No pesticides of interest were detected in 30 real samples collected across the nation. However, the data can be provided for establishing maximum residue limits for the pesticides in mealworms in response to the positive list system.

This study determined residual buprofezin levels in fresh ginseng and evaluated their changes during processing. Supervised field trials were conducted at Yeongju, Geumsan, and Goesan, Korea. Buprofezin 12.5% EC was applied to 5-y ginseng in accordance with the Korean good agriculture practice (GAP). Samples were collected at 0, 7, 14, 21, and 30 d after the final application. On day 14 (GAP-equivalent preharvest date), the ginseng was processed to obtain dried and red ginseng. The average buprofezin concentrations on day 0 were 0.076 (Yeongju), 0.055 (Geumsan), and 0.078 mg kg⁻¹ (Goesan). Residual concentrations increased as ginseng was processed into dried and red ginseng. Residue levels in dried ginseng manufactured by hot air drying were higher than in red ginseng obtained by steaming, hot air, and sunlight drying. However, the absolute amount of pesticides decreased by approximately 20–30% as a result of calculating the reduction factor considering the dry yield and moisture content. Therefore, the residual concentration in processed products may vary depending on the processing method, and it is deemed necessary to consider the processing yield and moisture content when evaluating the safety of residual pesticides in dried processed products.