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As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative spectral method shows great potential in retrieving LPPC. However, the performance of fractional derivatives and machine learning (ML) for retrieving apple LPPC still needs to be explored. The objective of this study is to test the capacity of using fractional derivative and ML methods to retrieve apple LPPC. Here, the hyperspectral data in the 400–2500 nm domains was used to calculate the fractional derivative order of 0.2–2, and then the sensitive bands were screened through feature dimensionality reduction to train ML to build the LPPC estimation model. Additionally, VIs-based ML methods and empirical regression models were developed to compare with the fractional derivative methods. The results showed that fractional derivative-driven ML methods have higher accuracy than the ML methods driven by the original spectra or vegetation index. The results also showed that the ML methods perform better than empirical regression models. Specifically, the best estimates of chlorophyll content and carotenoid content were achieved using support vector regression (SVR) at the derivative order of 0.2 (R2 = 0.78) and 0.4 (R2 = 0.75), respectively. The fractional derivative maintained a good universality in retrieving the LPPC of multiple phenological periods. Therefore, this study highlights that the fractional derivative and ML improved the estimation of apple LPPC. [ABSTRACT FROM AUTHOR]

Precise fertilization of rice depends on the timely and effective acquisition of fertilizer application recommended by prescription maps in large-scale cropland, which can provide fertilization spatial information reference. In this paper, the prescription map was discussed based on the improved nitrogen fertilizer optimization algorithm (NFOA), using satellite and unmanned aerial vehicle (UAV) imagery, and supplemented by meteorological data. Based on the principles of NFOA, firstly, remote sensing data and meteorological data were collected from 2019 to 2021 to construct a prediction model for the potential yield of rice based on the in-season estimated yield index (INSEY). Secondly, based on remote sensing vegetation indices (VIs) and spectral features of bands, the grain nitrogen content (GNC) prediction model constructed using the Random Forest (RF) algorithm was used to improve the values of GNC taken in the NFOA. The nitrogen demand for rice was calculated according to the improved NFOA. Finally, the nitrogen fertilizer application recommended prescription map of rice in large-scale cropland was generated based on UAV multispectral images, and the economic cost-effectiveness of the prescription map was analyzed. The analysis results showed that the potential yield prediction model of rice based on the improved INSEY had a high fitting accuracy (R2 = 0.62). The accuracy of GNC estimated with the RF algorithm reached 96.3% (RMSE = 0.07). The study shows that, compared with the non-directional and non-quantitative conventional tracking of N fertilizer, the recommended prescription map based on the improved NFOA algorithm in large-scale cropland can provide accurate information for crop N fertilizer variable tracking and provide effective positive references for the economic benefits of rice and ecological benefits of the field environment. [ABSTRACT FROM AUTHOR]

Crop residue is an important component of farmland ecosystems, which is of great significance for increasing soil organic carbon, mitigating wind erosion and water erosion and conserving soil and water. Crop residue coverage (CRC) is an important parameter to characterize the number and distribution of crop residues, and also a key indicator of conservation tillage. In this study, the CRC of wheat was taken as the research object. Based on the high-resolution GF-1 satellite remote sensing imagery from China, decision tree (DT), gradient boosting decision tree (GBDT), random forest (RF), least absolute shrinkage and selection operator (LASSO), extreme gradient boosting regression (XGBR) and other machine learning algorithms were used to carry out the estimation of wheat CRC by remote sensing. In addition, the comparisons with sentinel-2 imagery data were also utilized to assess the potential of GF satellite data for CRC estimates. The results show the following: (1) Among the spectral indexes using shortwave infrared characteristic bands from sentinel-2 imagery, the dead fuel index (DFI) was the best for estimating wheat CRC, with an R2 of 0.54 and an RMSE of 10.26%. The ratio vegetation index (RVI) extracted from visible and near-infrared characteristic bands from GF-1 data performed the best, with an R2 of 0.46 and an RMSE of 11.39%. The spectral index extracted from GF-1 and sentinel-2 images had a significant response relationship with wheat residue coverage. (2) When only the characteristic bands from the visible and near-infrared spectral ranges were applied, the effects of the spatial resolution differences of different images on wheat CRC had to be taken into account. The estimations of wheat CRC with the high-resolution GF-1 data were significantly better than those with the Sentinel-2 data, and among multiple machine learning algorithms adopted to estimate wheat CRC, LASSO had the most stable capability, with an R2 of 0.46 and an RMSE of 11.4%. This indicates that GF-1 high-resolution satellite imagery without shortwave infrared bands has a good potential in applications of monitoring crop residue coverage for wheat, and the relevant technology and method can also provide a useful reference for CRC estimates of other crops. [ABSTRACT FROM AUTHOR]

Early warning of plant diseases and pests is critical to ensuring food safety and production for economic crops. Data sources such as the occurrence, frequency, and infection locations are crucial in forecasting plant diseases and pests. However, at present, acquiring such data relies on fixed-point observations or field experiments run by agricultural institutions. Thus, insufficient data and low rates of regional representative are among the major problems affecting the performance of forecasting models. In recent years, the development of mobile internet technology and conveniently accessible multi-source agricultural information bring new ideas to plant diseases' and pests' forecasting. This study proposed a forecasting model of Alternaria Leaf Spot (ALS) disease in apple that is based on mobile internet disease survey data and high resolution spatial-temporal meteorological data. Firstly, a mobile internet-based questionnaire was designed to collect disease survey data efficiently. A specific data clean procedure was proposed to mitigate the noise in the data. Next, a sensitivity analysis was performed on the temperature and humidity data, to identify disease-sensitive meteorological factors as model inputs. Finally, the disease forecasting model of the apple ALS was established using four machine learning algorithms: Logistic regression(LR); Fisher linear discriminant analysis(FLDA); Support vector machine(SVM); and K-Nearest Neighbors (KNN). The KNN algorithm is recommended in this study, which produced an overall accuracy of 88%, and Kappa of 0.53. This paper shows that through mobile internet disease survey and a proper data clean approach, it is possible to collect necessary data for disease forecasting in a short time. With the aid of high resolution spatial-temporal meteorological data and machine learning approaches, it is able to achieve disease forecast at a regional scale, which will facilitate efficient disease prevention practices. [ABSTRACT FROM AUTHOR]