The integrated use of plant-derived volatile attractants and synthetic insecticides in attract-and-kill programs is a useful tool for integrated pest management programs reducing pesticide input. Efficient alternative insecticides are critically needed to replace methomyl, which has been banned on cruciferous vegetables in China because it is also highly toxic to nontarget organisms. In the present study, among 15 commonly used insecticides were screened for toxicity against S. litura moths, where chlorantraniliprole, flubendiamide, and emamectin benzoate was found to have the highest levels of toxicity (LC₅₀ of 0.56, 3.85, and 6.03 mg a.i. L⁻¹ respectively). After exposure to the low lethal concentration LC₅₀ of chlorantraniliprole, fecundity of the moths was substantially reduced. Egg-hatching was lower for LC₂₀- and LC₅₀-treated moth pairs than for untreated control pairs. Net reproductive rate (R₀), intrinsic rate of increase (r), and finite rate of increase (λ) were significantly reduced in LC₅₀♀ × LC₅₀♂ cohorts. Larval mortality was significantly higher in subsequent generations in pairs of LC₅₀-treated moths. Chlorantraniliprole, which was most toxic and had significant sublethal effects on moths, can be used as an alternative insecticide to methomyl in the attracticide for controlling S. litura moths, and the LC₅₀ indicated a high potential for efficacy in the control S. litura through attract-and-kill schemes.

Diaphorina citri Kuwayama (D. citri) is one of the major pests in the citrus industry, which spreads Citrus Huanglongbing disease. It has developed resistance to chemical insecticides. Therefore, searching for greener solutions for pest management is critically important. The main aim of this study was to evaluate the repellent and insecticidal efficacy of essential oils (EOs) from four species of Myrtaceae plants: Psidium guajava (PG), Eucalyptus robusta (ER), Eucalyptus tereticornis (ET), and Baeckea frutescens (BF) against D. citri and to analyze their chemical compositions. GC-MS analysis was performed, and the results indicated that the EOs of PG, ER, ET, and BF were rich in terpenoids, ketones, esters, and alcohol compounds. The repellent rate of all four EOs showed that it decreased with exposure time but increased with the concentration of EOs from 80.50% to 100.00% after treating D. citri for 6 h with four EOs at 100% concentration and decreased to 67.71% to 85.49% after 24 h of exposure. Among the compounds from the EOs tested, eucalyptol had the strongest repellent activity, with a 24 h repellency rate of 100%. The contact toxicity bioassay results showed that all EOs have insecticidal toxicity to D. citri; the LC50 for nymphs was 36.47–93.15 mL/L, and for adults, it was 60.72–111.00 mL/L. These results show that when PG is used as the reference material, the ER, ET, and BF EOs have strong biological activity against D. citri, which provides a scientific basis for the further development of plant-derived agrochemicals.

Huanglongbing (HLB) is one of the most destructive diseases in citrus production worldwide. Early detection of HLB pathogens can facilitate timely removal of infected citrus trees in the field. However, low titer and uneven distribution of HLB pathogens in host plants make reliable detection challenging. Therefore, the development of effective detection methods with high sensitivity is imperative. This study reports the development of a novel method, tandem repeat-based polymerase chain displacement reaction (TR-PCDR), for the detection of ‘Candidatus Liberibacter asiaticus’, a widely distributed HLB-associated bacterium. A uniquely designed primer set (TR2-PCDR-F/TR2-PCDR-1R) and a thermostable Taq DNA polymerase mutant with strand displacement activity were used for TR-PCDR amplification. Performed in a regular thermal cycler, TR-PCDR could produce more than two amplicons after each amplification cycle. Sensitivity of the developed TR-PCDR was 10 copies of target DNA fragment. The sensitive level was proven to be 100× higher than conventional PCR and similar to real-time PCR. Data from the detection of ‘Ca. L. asiaticus’ with filed samples using the above three methods also showed similar results. No false-positive TR-PCDR amplification was observed from healthy citrus samples and water controls. These results thereby illustrated that the developed TR-PCDR method can be applied to the reliable, highly sensitive, and cost-effective detection of ‘Ca. L. asiaticus’.

Siraitia grosvenorii, known as “Luohanguo or monk fruit”, is a perennial vine belonging to the family Cucurbitaceae. It is cultivated for its fruits, which are used as a Chinese traditional medicin...

Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield estimation. Currently, most crop yield estimation studies based on the YOLO model are only conducted during a single stage of maturity. Combining multi-growth period data for crop analysis is of great significance for crop growth detection and early yield estimation. In this study, a new network model, YOLOv8-RL, was proposed using citrus multigrowth period characteristics as a data source. A citrus yield estimation model was constructed and validated by combining network identification counts with manual field counts. Compared with YOLOv8, the number of parameters of the improved network is reduced by 50.7%, the number of floating-point operations is decreased by 49.4%, and the size of the model is only 3.2 MB. In the test set, the average recognition rate of citrus flowers, green fruits, and orange fruits was 95.6%, the mAP@.5 was 94.6%, the FPS value was 123.1, and the inference time was only 2.3 milliseconds. This provides a reference for the design of lightweight networks and offers the possibility of deployment on embedded devices with limited computational resources. The two estimation models constructed on the basis of the new network had coefficients of determination R² values of 0.91992 and 0.95639, respectively, with a prediction error rate of 6.96% for citrus green fruits and an average error rate of 3.71% for orange fruits. Compared with network counting, the yield estimation model had a low error rate and high accuracy, which provided a theoretical basis and technical support for the early prediction of fruit yield in complex environments.

The leaf water content (LWC) of citrus is a pivotal indicator for assessing citrus water status. Addressing the limitations of traditional hyperspectral modelling methods, which rely on single preprocessing techniques and struggle to fully exploit the complex information within spectra, this study proposes a novel strategy for estimating citrus LWC by integrating spectral preprocessing combinations with an enhanced deep learning architecture. Utilizing a citrus plantation in Guangxi as the experimental site, 240 leaf samples were collected. Seven preprocessing combinations were constructed based on multiplicative scatter correction (MSC), continuous wavelet transform (CWT), and first derivative (1st D), and a new multichannel network, EDPNet (Ensemble Data Preprocessing Network), was designed for modelling. Furthermore, this study incorporated an attention mechanism within EDPNet, comparing the applicability of SE Block, SAM, and CBAM in integrating spectral combination information. The experiments demonstrated that (1) the triple preprocessing combination (MSC + CWT + 1st D) significantly enhanced model performance, with the prediction set R² reaching 0.8036, a 13.86% improvement over single preprocessing methods, and the RMSE reduced to 2.3835; (2) EDPNet, through its multichannel parallel convolution and shallow structure design, avoids excessive network depth while effectively enhancing predictive performance, achieving a prediction accuracy (R² = 0.8036) that was 5.58–9.21% higher than that of AlexNet, VGGNet, and LeNet-5, with the RMSE reduced by 9.35–14.65%; and (3) the introduction of the hybrid attention mechanism CBAM further optimized feature weight allocation, increasing the model’s R² to 0.8430 and reducing the RMSE to 2.1311, with accuracy improvements of 2.08–2.36% over other attention modules (SE, SAM). This study provides a highly efficient and accurate new method for monitoring citrus water content, offering practical significance for intelligent orchard management and optimal resource allocation.