Weiping Kong

Banana Fusarium wilt (BFW, also known as Panama disease) is a highly infectious and destructive disease that threatens global banana production, requiring early recognition for timely prevention and control. Current monitoring methods primarily rely on continuous variable features—such as band reflectances (BRs) and vegetation indices (VIs)—collectively referred to as basic features (BFs)—which are prone to noise during the early stages of infection and struggle to capture subtle spectral variations, thus limiting the recognition accuracy. To address this limitation, this study proposes a discretized enhanced feature (EF) construction method, the automated kernel density segmentation-based feature construction algorithm (AutoKDFC). By analyzing the differences in the kernel density distributions between healthy and diseased samples, the AutoKDFC automatically determines the optimal segmentation threshold, converting continuous BFs into binary features with higher discriminative power for early-stage recognition. Using UAV-based multi-spectral imagery, BFW recognition models are developed and tested with the random forest (RF), support vector machine (SVM), and Gaussian naïve Bayes (GNB) algorithms. The results show that EFs exhibit significantly stronger correlations with BFW’s presence than original BFs. Feature importance analysis via RF further confirms that EFs contribute more to the model performance, with VI-derived features outperforming BR-based ones. The integration of EFs results in average performance gains of 0.88%, 2.61%, and 3.07% for RF, SVM, and GNB, respectively, with SVM achieving the best performance, averaging over 90%. Additionally, the generated BFW distribution map closely aligns with ground observations and captures spectral changes linked to disease progression, validating the method’s practical utility. Overall, the proposed AutoKDFC method demonstrates high effectiveness and generalizability for BFW recognition. Its core concept of “automatic feature enhancement” has strong potential for broader applications in crop disease monitoring and supports the development of intelligent early warning systems in plant health management.

This study employed an integrated approach combining multi-source remote sensing data and the MaxEnt model to systematically assess the ecological niche characteristics of the oriental migratory locust (Locusta migratoria manilensis) in Hainan Island, while projecting the evolution of its suitable habitats under both historical and future climate scenarios (up to 2040). Firstly, we synthesized traditional climate, soil, and topography data with remote sensing data to characterize the suitable areas of the oriental migratory locust based on MaxEnt model (with high accuracy of AUC = 0.935 and TSS = 0.76). Subsequently, six dominant environmental variables—precipitation in April (PRE04), precipitation in September (PRE09), maximum temperature in August (TMAX08), minimum temperature in December (TMIN12), NDVI in February (NDVI02), and NDVI in May (NDVI02)—were identified as key predictors. Their threshold values were determined, with PRE04, PRE09, TMAX08, and TMIN12 ranging from 39 to 44 mm, 196 to 223 mm, 31.1 to 32.2 °C, and 17.7 to 18.0 °C in high-suitability zones, respectively. Finally, these six predictors were used to assess habitat suitability across Hainan Island for both the 2001–2020 and 2021–2040 periods. Under historical climate conditions, highly suitable areas (505 km², 1.41% of total land area) were concentrated in the western and northeastern regions, particularly in Dongfang City (46.27%), Ledong Li Autonomous County (32.91%), and Changjiang Li Autonomous County (18.39%). Future projections indicate significant habitat expansion, with total suitable areas increasing by 13.4–42.0% and highly suitable areas reaching 571–831 km² by 2040. The study highlights the critical Dongfang–Danzhou–Ledong region for targeted locust control, providing scientific support for pest management in tropical island ecosystems under climate change.

Introduction: Nondestructive quantification of leaf chlorophyll content (LCC) of banana and its spatial distribution across growth stages from remotely sensed data provide an effective avenue to diagnose nutritional deficiency and guide management practices. Unmanned aerial vehicle (UAV) hyperspectral imagery can document abundant texture features (TFs) and spectral information in a field experiment due to the high spatial and spectral resolutions. However, the benefits of using the fine spatial resolution accessible from UAV data for estimating LCC for banana have not been adequately quantified.Methods: In this study, two types of image features including vegetation indices (VIs) and TFs extracted from the first-three-principal-component-analyzed images (TFs-PC1, TFs-PC2, and TFs-PC3) were employed. We proposed two methods of image feature combination for banana LCC inversion, which are a two-pair feature combination and a multivariable feature combination based on four machine learning algorithms (MLRAs).Results: The results indicated that compared to conventionally used VIs alone, the banana LCC estimations with both proposed VI and TF combination methods were all significantly improved. Comprehensive analyses of the linear relationships between all constructed two-pair feature combinations and LCC indicated that the ratio of mean to modified red-edge sample ratio index (MEA/MSR_re) stood out (R² = 0.745, RMSE = 2.17). For multivariable feature combinations, four MLRAs using original or two selected VIs and TFs-PC1 combination groups resulted in better LCC estimation than the other input variables. We concluded that the nonlinear Gaussian process regression model with the VIs and TFs-PC1 combination selected by maximal information coefficient as input achieved the highest accuracy in LCC prediction for banana, with the highest R² of 0.776 and lowest RMSE of 2.04. This study highlights the potential of the proposed image feature combination method for deriving high-resolution maps of banana LCC fundamental for precise nutritional diagnosing and operational agriculture management.

Grasshoppers mainly threaten natural grassland vegetation and crops. Therefore, it is of great significance to understand the relationship between environmental factors and grasshopper occurrence. This paper studies the spatial distribution and key factors of grasshopper occurrence in two grass types by integrating a machine learning model (Maxent) and remote sensing data within the major grasshopper occurrence areas of Inner Mongolia, China. The modelling results demonstrate that the typical steppe has larger suitable area and more proportion for grasshopper living than meadow steppe. The soil type, above biomass, altitude and temperature mainly determine the grasshopper occurrence in typical steppe and meadow steppe. However, the contribution of these factors in the two grass types is significantly different. In addition, related vegetation and meteorological factors affect the different growing stages of grasshoppers between the two grass types. This study clearly defines the different effects of key environmental factors (meteorology, vegetation, soil and topography) for grasshopper occurrence in typical steppe and meadow steppe. It also provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be helpful for future management measures, to ensure grass ecological environment security and the sustainable development of grassland.

Canopy chlorophyll content (CCC) indicates the photosynthetic functioning of a crop, which is essential for the growth and development and yield increasing. Accurate estimation of CCC from remote-sensing data benefits from including information on leaf chlorophyll and canopy structures. However, conventional nadir reflectance is usually subject to the lack of an adequate expression on the geometric structures and shaded parts of vegetation canopy, and the derived vegetation indices (VIs) are prone to be saturated at high CCC level. Using 3-year field experiments with different wheat cultivars, leaf colors, structural types, and growth stages, and integrated with PROSPECT+SAILh model simulation, we studied the potential of multi-angle reflectance data for the improved estimation of CCC. The characteristics of angular anisotropy in spectral reflectance were investigated. Analyses based on both simulated and experimental multi-angle hyperspectral data were carried out to compare performances of 20 existing VIs at different viewing angles, and to propose an algorithm to develop novel biangular-combined vegetation indices (BCVIs) for tracking CCC dynamics in wheat. The results indicated that spectral reflectance values, as well as the coefficient of determination (R²) between mono-angular VIs and CCC, at back-scattering directions, were mostly higher than those at forward-scattering directions. Mono-angular VIs at +30° angle, were closest to the hot-spot position in our case, achieved the highest R² among 13 viewing angles including the nadir observation. The general formulation for the newly developed BCVIs was BCVI_VI = f × VI_(θ1) − (1 − f) × VI_(θ2), in which the VI was used to characterize chlorophyll status, while the subtraction of VI at θ1 and θ2 viewing angles in a proportion was used to highlight the canopy structural information. From our result, the values of the θ1 and θ2 around hot-spot and dark-spot positions, and the f of 0.6 or 0.7 were found as the optimized values. Through comparisons revealed that large improvements on CCC modeling could be obtained by the BCVIs, especially for the experimental data, indicated by the increase in R² by 25.1–51.4%, as compared to the corresponding mono-angular VIs at +30° angle. The BCVI_{MCARI[705,750]} was proved to greatly undermine the saturation effect of mono-angular MCARI[705,750], expressing the best linearity and the most sensitive to CCC, with R² of 0.98 and 0.72 for simulated and experimental data, respectively. Our study will eventually have extensive prospects in monitoring crop phenotype dynamics in for example large breeding trials.

Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R² = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R² of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.

Heterogeneity exists in the vertical distribution of the biochemical components of crops. A leaf chlorophyll deficiency occurs in the bottom- and middle-layers of crops due to nitrogen stress and leaf senescence. Some studies used multi-angular remote sensing data for estimating the vertical distribution of the leaf chlorophyll content (LCC). However, these studies performed LCC inversion of different vertical layers using a fixed view zenith angle (VZA), but rarely considered the contribution of the components of the non-target layers to the spectral response. The main goal of this work was to determine the LCC of different vertical layers of the canopy of winter wheat (Triticum aestivum L.), using multi-angular remote sensing and spectral vegetation indices. Different combinations of VZAs were used for obtaining the LCC of different layers. The results revealed that the responses of the transformed chlorophyll in reflectance absorption index (TCARI) and modified chlorophyll absorption in reflectance index (MCARI)/optimized soil-adjusted vegetation index (OSAVI) to the upper-layer LCC were strongest at VZA 10°. For the middle-layer LCC, the response was strongest at 30°, but the response was significantly lower than that of the upper-layer. For the bottom-layer LCC, the responses were weak due to the obscuring effect of the upper- and middle-layer; thus, the LCC inversion of the bottom-layer data was not optimal for a single VZA. The optimal VZA or VZA combinations for LCC estimation were VZA 10° for the upper-layer LCC (TCARI with coefficient of determination (R²) = 0.69, root mean square error (RMSE) = 4.80 ug/cm², MCARI/OSAVI with R² = 0.73, RMSE = 4.17 ug/cm²), VZA 10° and 30° for the middle-layer LCC (TCARI with R² = 0.17, RMSE = 4.81 ug/cm², MCARI/OSAVI with R² = 0.17, RMSE = 4.76 ug/cm²), and VZA 10°, 30°, and 50° for the bottom-layer LCC (TCARI with R² = 0.40, RMSE = 6.29 ug/cm², MCARI/OSAVI with R² = 0.40, RMSE = 6.36 ug/cm²). The proposed observation strategy provided a significantly higher estimation accuracy of the target layer LCC than the single VZA approach, and demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the wheat canopy chlorophyll content vertical distribution.

Remote sensing approaches have several advantages over traditional methods in determining information on physical and chemical parameters, including timely data acquisition, low costs, and wide coverage. Thus, remote sensing is widely used in crop growth monitoring. Unlike vertical observations, multi-angular remote sensing technology can obtain the vertical distribution information of the central and lower leaves of a crop. Furthermore, applications of remote sensing on the vertical distribution of maize canopy components is complicated, and related research is limited. In the current paper, we employed multi-angular spectral data, measured by a self-designed multi-angular observation instrument at view zenith angles (VZAs) of 0°, 10°, 20°, 30°, 40°, 50°, and 60°, to explore the monitoring strategy and monitoring precision of the vertical distribution of chlorophyll content in the maize canopy. This was then used to determine the optimal monitoring method for the chlorophyll content (soil and plant analyzer development (SPAD) value) of each layer. The correlation between SPAD value and chlorophyll sensitivity indices at different growth stages was used as the basis for screening indices and VZAs. The correlation between the selected EPI (eucalyptus pigment index) and REIP (red edge inflection point) indices and chlorophyll content indicated view zenith angles (VZAs) of 0°, 30°, and 40° as optimal for the early growth stage monitoring of chlorophyll content in the 1st, 2nd, and 3rd layers, respectively. These values were associated with RMSEs of 4.14, 1.71, and 1.11 for EPI, respectively; and 4.61, 2.31, and 1.00 for REIP, respectively. In addition, a VZA of 50° was selected to monitor the chlorophyll content of the 1st, 2nd, 3rd, and 4th layers at the late growth stage, with RMSE values of 2.97, 3.50, 2.80, and 4.80 for EPI, respectively; and 3.16, 5.02, 4.55, and 7.85 for REIP, respectively. The results demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the maize canopy chlorophyll content vertical distribution, with the VZAs of different vertical layers varying between the early and late growth stages.

The application of chemical harvest aids to defoliate leaves and ripen bolls plays a significant role in the once-over machine harvest of cotton (Gossypium hirsutum L.) fields. The boll opening rate (BOR) is a key indicator for the determination of harvest aid spraying times. However, the most commonly used method to determine BOR is manual investigation, which is subjective and cannot have a holistic judgment of the entire area. Remote sensing can be employed to overcome these limitations, due to a wide field of vision, acceptably spatial and temporal resolution, and rich spectral information beyond the perception of the human eye. The reflectance of open cotton bolls is relatively high in the visible and near-infrared bands. High reflectance of open bolls has a great influence on the reflectance of the mixed pixels on remote sensing imagery. Therefore, it is an effective method to detect boll opening status by constructing vegetation indices with the sensitive spectral bands of imagery. In this study, we proposed two new vegetation indices based on Sentinel-2 remote sensing data, namely, the boll area ratio index (BARI) and the boll opening rate index (BORI), in order to estimate the boll opening status on a regional scale. The proposed indices were strongly correlated with the boll area ratio (BAR) and BOR. In particular, BARI exhibited the most accurate and robust performance with BAR in the prediction (R² = 0.754, RMSE = 2.56%) and validation (R² = 0.706, RMSE = 5.00%) among all the indices, including published indices we chose. Furthermore, when comparing to all other indices, BORI demonstrated the best and satisfactory estimation with BOR in the prediction (R² = 0.675, RMSE = 7.96%) and validation (R² = 0.616, RMSE = 2.79%). Meanwhile, an exponential growth relationship between BOR and BAR was identified, and the underlying mechanisms behind this phenomenon were discussed. Overall, through our study, we provided convenient and accurate vegetation indices for the investigation of boll opening status in a cotton-producing area by accessible and free Sentinel-2 imagery.