Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing.

Remote Sensing Monitoring of Rice Diseases and Pests from Different Data Sources: A Review

Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in scale. Spectral and imaging technologies in the visible and near-infrared (Vis/NIR) region have been widely investigated for crop disease detection. This work explored the potential of integrating canopy reflectance spectra acquired near the ground and aerial multispectral images captured with an unmanned aerial vehicle (UAV) for estimating Soil-Plant Analysis Development (SPAD) values and detecting rice leaf blast disease in the field. Canopy reflectance spectra were preprocessed, followed by effective band selection. Different vegetation indices (VIs) were calculated from multispectral images and selected for model establishment according to their correlation with SPAD values and disease severity. The full-wavelength canopy spectra (450–850 nm) were first used for establishing SPAD inversion and blast disease classification models, demonstrating the effectiveness of Vis/NIR spectroscopy for SPAD inversion and blast disease detection. Then, selected effective bands from the canopy spectra, UAV VIs, and the fusion of the two data sources were used for establishing corresponding models. The results showed that all SPAD inversion models and disease classification models established with the integrated data performed better than corresponding models established with the single of either of the aerial and ground data sources. For SPAD inversion models, the best model based on a single data source achieved a validation determination coefficient (Rcv2) of 0.5719 and a validation root mean square error (RMSECV) of 2.8794, while after ground and aerial data fusion, these two values improved to 0.6476 and 2.6207, respectively. For blast disease classification models, the best model based on a single data source achieved an overall test accuracy of 89.01% and a Kappa coefficient of 0.86, and after data fusion, the two values improved to 96.37% and 0.95, respectively. These results indicated the significant potential of integrating canopy reflectance spectra and UAV multispectral images for detecting rice diseases in large fields. 

Detection of Rice Leaf SPAD and Blast Disease Using Integrated Aerial and Ground Multiscale Canopy Reflectance Spectroscopy

Due to the adverse effect of prolonged drought stress on plants, accurate detection is essential for water use efficiency and maintaining productivity. Hyperspectral imaging is frequently used for non-invasive plant phenotyping, allowing for the long-term monitoring of crop health due to its sensitivity to subtle changes in leaf constituents. The broad spectrum of hyperspectral data enables the development of multiple vegetation indices (Vis) derived from the different spectral regions to estimate plant biophysical and biochemical traits. However, the known VIs often do not generalize well and perform poorly for multiple plant stresses. This study proposes new VIs combined with machine learning models to identify drought stress in wheat species under different nitrogen (N) levels. A wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high N and drought-stress low-N. In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) were taken regularly, serving as the ground truth data for this study. Sensitive spectral features were selected using a custom-designed ensemble modelling technique. New drought VIs are proposed using different combinations of the selected features. Three classification models (support vector machines, random forest and deep neural network) were developed and trained using four sets of data: known VIs, proposed VIs, combined VIs (from the known and proposed VIs) and PCA-transformed features (over the whole spectral data). From the results, the proposed VIs outperformed the known VIs, yielding &amp;amp;gt; 0.94 accuracies for all three models, and the performance improved when they were trained with the combined VIs. The combined VIs were used to train three regression models to predict the stomatal conductances and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. 

Hyperspectral Imaging for Phenotyping Plant Drought Stress Using Univariate and Multivariate Modelling Techniques

Blast is a rice disease that affects all above-ground parts of the plant, such as nodes, stems, panicles, leaves, etc. Damage to these plant organs negatively affects the developmental stage, that is, the growing season of the plant. As a result, it is the reason for the formation of puny seeds and decreased productivity. The creation of lines and varieties resistant to blast disease is an urgent problem for all regions involved in rice growing. The reason is the rapid adaptation of the pathogen to environmental conditions, as well as to the immune defense of plants. This research includes results of crossing domestic highly productive zoned varieties with donor varieties of blast resistance genes. Three lines have been identified that carry the blast resistance gene Pi b in their genotype. To assess the productivity of the resulting lines, the elements of the crop structure were assessed and the preservation of productivity by the new lines was determined.

https://www.bio-conferences.org/articles/bioconf/pdf/2024/19/bioconf_ifbioscfu2024_03012.pdf

Molecular screening for the presence of Magnaporthe oryzae resistance Pi-b gene in rice hybrids

A comprehensive study on “Improvement of production and storage system for Gobindabhog rice in West Bengal,” comprising two field and one laboratory experiment, was done at Bidhan Chandra Krishi Viswavidyalaya, Kalyani, Nadia, West Bengal, India, during the period of 2010–2012 to find out the optimum planting time, spacing, nutrients, disease, and pest management for scented Gobindabhog rice during the kharif season. Plant height, tiller production, and leaf area index increased consistently with the advancement of crop growth up to panicle exertion, panicle initiation (i.e., 56 DAT), and flowering (i.e., 84 DAT) stages, respectively. The planting date had a significant effect on the incidence of blast disease in Gobindabhog rice during both 2010 and 2011, except at 28 DAT. Delay in planting generally provided a favorable environment for the causal organism (Payricularia oryzae) during the vegetative and reproductive stages of Gobindabhog rice, thereby leading to a greater incidence of delayed crops (25 July, 10 August, and 25 August) than the earlier one (10 July). The susceptibility of the Gobindabhog rice crop to brown spot disease caused by Drechslera oryzae increased progressively with the advancement of crop age from 28 DAT (i.e., active tillering stage) to 84 DAT (i.e., flowering stage). The population of gundhi bug (Leptocorisa acuta) increased steadily from 84 DAT (i.e., 50% flowering) to 112 DAT (maturity stage), irrespective of main and sub-plot treatments as well as years of investigation.

Study on Major Disease-insect Incidence of Scented Gobindabhog Rice in New Alluvial Zone of West Bengal

Rice is the staple food of more than half of the population of the world and India as well. One of the major constraints in rice production is frequent occurrence of pests and diseases and one of them is rice blast which often causes yield loss varying from 10 to 30%. Conventional approaches for disease assessment are time-consuming, expensive, and not real-time; alternately, sensor-based approach is rapid, non-invasive and can be scaled up in large areas with minimum time and effort. In the present study, hyperspectral remote sensing for the characterization and severity assessment of rice blast disease was exploited. Field experiments were conducted with 20 genotypes of rice having sensitive and resistant cultivars grown under upland and lowland conditions at Almora, Uttarakhand, India. The severity of the rice blast was graded from 0 to 9 in accordance to International Rice Research Institute (IRRI). Spectral observations in field were taken using a hand-held portable spectroradiometer in range of 350-2500 nm followed by spectral discrimination of different disease severity levels using Jeffires–Matusita (J-M) distance. Then, evaluation of 26 existing spectral indices (r≥0.8) was done corresponding to blast severity levels and linear regression prediction models were also developed. Further, the proposed ratio blast index (RBI) and normalized difference blast index (NDBI) were developed using all possible combinations of their correlations with severity level followed by their quantification to identify the best indices. Thereafter, multivariate models like support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) were also used to estimate blast severity. Jeffires–Matusita distance was separating almost all severity levels having values >1.92 except levels 4 and 5. The 26 prediction models were effective at predicting blast severity with R2 values from 0.48 to 0.85. The best developed spectral indices for rice blast were RBI (R1148, R1301) and NDBI (R1148, R1301) with R2 of 0.85 and 0.86, respectively. Among multivariate models, SVM was the best model with calibration R2=0.99; validation R2=0.94, RMSE=0.7, and RPD=4.10. The methodology developed paves way for early detection and large-scale monitoring and mapping using satellite remote sensors at farmers’ fields for developing better disease management options.

/pdf/spectral-characterization-and-severity-assessment-of-rice-3ioycw7k.pdf

Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models

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Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models