Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376–1044 nm) and near-infrared (NIR) (915–1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, the best model was ResNet, with the accuracy of over 93%. For NIR spectra, LR was the best, with the accuracy of over 97%, but SVM, CNN, and ResNet also showed closed and fine results. The saliency map of CNN and ResNet presented similar and closed ranges of crucial wavelengths. Overall results indicated deep learning performed better than conventional machine learning. The study showed that the use of hyperspectral imaging technology combined with machine learning can effectively detect the level of pesticide residues in grapes.

Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.

Firmness, soluble solid content (SSC) and titratable acidity (TA) are characteristic substances for evaluating the quality of cherry tomatoes. In this paper, a hyper spectral imaging (HSI) system using visible/near-infrared (Vis-NIR) and near-infrared (NIR) was proposed to detect the key qualities of cherry tomatoes. The effects of individual spectral information and fused spectral information in the detection of different qualities were compared for firmness, SSC and TA of cherry tomatoes. Data layer fusion combined with multiple machine learning methods including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BP) is used for model training. The results show that for firmness, SSC and TA, the determination coefficient R² of the multi-quality prediction model established by Vis-NIR spectra is higher than that of NIR spectra. The R² of the best model obtained by SSC and TA fusion band is greater than 0.9, and that of the best model obtained by the firmness fusion band is greater than 0.85. It is better to use the spectral bands after information fusion for nondestructive quality detection of cherry tomatoes. This study shows that hyperspectral imaging technology can be used for the nondestructive detection of multiple qualities of cherry tomatoes, and the method based on the fusion of two spectra has a better prediction effect for the rapid detection of multiple qualities of cherry tomatoes compared with a single spectrum. This study can provide certain technical support for the rapid nondestructive detection of multiple qualities in other melons and fruits.

Cucumber (Cucumis sativus L.) is one of the most popular cultivated vegetable crops but it is intrinsically sensitive to cold stress due to its thermophilic nature. To explore the molecular mechanism of plant response to low temperature (LT) and the mitigation effect of exogenous nitric oxide (NO) on LT stress in cucumber, transcriptome changes in cucumber leaves were compared. The results showed that LT stress regulated the transcript level of genes related to the cell cycle, photosynthesis, flavonoid accumulation, lignin synthesis, active gibberellin (GA), phenylalanine metabolism, phytohormone ethylene and salicylic acid (SA) signaling in cucumber seedlings. Exogenous NO improved the LT tolerance of cucumber as reflected by increased maximum photochemical efficiency (Fv/Fm) and decreased chilling damage index (CI), electrolyte leakage and malondialdehyde (MDA) content, and altered transcript levels of genes related to phenylalanine metabolism, lignin synthesis, plant hormone (SA and ethylene) signal transduction, and cell cycle. In addition, we found four differentially expressed transcription factors (MYB63, WRKY21, HD-ZIP, and b-ZIP) and their target genes such as the light-harvesting complex I chlorophyll a/b binding protein 1 gene (LHCA1), light-harvesting complex II chlorophyll a/b binding protein 1, 3, and 5 genes (LHCB1, LHCB3, and LHCB5), chalcone synthase gene (CSH), ethylene-insensitive protein 3 gene (EIN3), peroxidase, phenylalanine ammonia-lyase gene (PAL), DNA replication licensing factor gene (MCM5 and MCM6), gibberellin 3 beta-dioxygenase gene (GA3ox), and regulatory protein gene (NPRI), which are potentially associated with plant responses to NO and LT stress. Notably, HD-ZIP and b-ZIP specifically responded to exogenous NO under LT stress. Taken together, these results demonstrate that cucumber seedlings respond to LT stress and exogenous NO by modulating the transcription of some key transcription factors and their downstream genes, thereby regulating photosynthesis, lignin synthesis, plant hormone signal transduction, phenylalanine metabolism, cell cycle, and GA synthesis. Our study unveiled potential molecular mechanisms of plant response to LT stress and indicated the possibility of NO application in cucumber production under LT stress, particularly in winter and early spring.

This study investigated the protective effects of exogenous ascorbic acid (AsA, 0.5 mmol·L⁻¹) treatment on salt-induced photosystem inhibition in tomato seedlings under salt stress (NaCl, 100 mmol·L⁻¹) conditions with and without the AsA inhibitor lycorine. Salt stress reduced the activities of photosystem II (PSII) and PSI. AsA treatment mitigated inhibition of the maximal photochemical efficiency of PSII (F_v/F_m), maximal P700 changes (P_m), the effective quantum yields of PSII and I [Y(II) and Y(I)], and non-photochemical quenching coefficient (NPQ) values under salt stress conditions both with and without lycorine. Moreover, AsA restored the balance of excitation energy between two photosystems (β/α-1) after disruption by salt stress, with or without lycorine. Treatment of the leaves of salt-stressed plants with AsA with or without lycorine increased the proportion of electron flux for photosynthetic carbon reduction [Je(PCR)] while decreasing the O₂-dependent alternative electron flux [Ja(O₂-dependent)]. AsA with or without lycorine further resulted in increases in the quantum yield of cyclic electron flow (CEF) around PSI [Y(CEF)] while increasing the expression of antioxidant and AsA–GSH cycle-related genes and elevating the ratio of reduced glutathione/oxidized glutathione (GSH/GSSG). Similarly, AsA treatment significantly decreased the levels of reactive oxygen species [superoxide anion (O₂⁻) and hydrogen peroxide (H₂O₂)] in these plants. Together, these data indicate that AsA can alleviate salt-stress-induced inhibition of PSII and PSI in tomato seedlings by restoring the excitation energy balance between the photosystems, regulating the dissipation of excess light energy by CEF and NPQ, increasing photosynthetic electron flux, and enhancing the scavenging of reactive oxygen species, thereby enabling plants to better tolerate salt stress.

Domestication and improvement are important processes that generate the variation in genome and phonotypes underlying crop improvement. Unfortunately, during selection for certain attributes, other valuable traits may be inadvertently discarded. One example is the decline in fruit soluble solid content (SSC) during tomato breeding. Several genetic loci for SSC have been identified, but few reports on the underlying mechanisms are available. In this study, we performed a genome-wide association study (GWAS) for SSC of the red-ripe fruits in a population consisting of 481 tomato accessions with large natural variations and found a new quantitative trait locus, STP1, encoding a Sugar Transporter Protein. The causal variation of STP1, a 21-bp InDel, located in promoter region 1,124 bp upstream of the start codon, alters its expression. STP1^Insertion accessions with 21-bp insertion have higher SSC than STP1^Deletion accessions with 21-bp deletion. Knockout of STP1 in TS-23 with high SSC using CRISPR/Cas9 greatly decreased SSC in fruits. In vivo and in vitro assays demonstrated ZAT10-LIKE, a zinc finger protein transcription factor (ZFP TF), can specifically bind to the promoter of STP1^Insertion to enhance STP1 expression, but not to the promoter of STP1^Deletion, leading to lower fruit SSC in modern tomatoes. Diversity analysis revealed that STP1 was selected during tomato improvement. Taken together, we identified a naturally occurring causal variation underlying SSC in tomato, and a new role for ZFP TFs in regulating sugar transporters. The findings enrich our understanding of tomato evolution and domestication, and provide a genetic basis for genome design for improving fruit taste.