Search Results (6)

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. Full article

The areas where landslides occur frequently pose severe threats to the local population, which necessitates conducting regional landslide susceptibility mapping (LSM). In this study, four models including weight-of-evidence (WoE) and three WoE-based models, which were linear discriminant analysis (LDA), Fisher’s linear discriminant analysis (FLDA), and quadratic discriminant analysis (QDA), were used to obtain the LSM in the Nanchuan region of Chongqing, China. Firstly, a dataset was prepared from sixteen landslide causative factors, including eight topographic factors, three distance-related factors, and five environmental factors. A landslide inventory map including 298 landslide locations was also constructed and randomly divided with a ratio of 70:30 as training and validation data. Subsequently, the WoE method was used to estimate the relationship between landslides and the landslide causative factors, which assign a weight value to each class of causative factors. Finally, four models were applied using the training dataset, and the predictive performance of each model was compared using the validation datasets. The results showed that FLDA had a higher performance than the other three models according to the success rate curve (SRC) and prediction rate curve (PRC), illustrating that it could be considered a promising approach for landslide susceptibility mapping in the study area. Full article

This analysis aims to generate landslide susceptibility maps (LSMs) using various machine learning methods, namely random forest (RF), alternative decision tree (ADTree) and Fisher’s Linear Discriminant Function (FLDA). The results of the FLDA, RF and ADTree models were compared with regard to their applicability for creating an LSM of the Gallicash river watershed in the northern part of Iran close to the Caspian Sea. A landslide inventory map was created using GPS points obtained in a field analysis, high-resolution satellite images, topographic maps and historical records. A total of 249 landslide sites have been identified to date and were used in this study to model and validate the LSMs of the study region. Of the 249 landslide locations, 70% were used as training data and 30% for the validation of the resulting LSMs. Sixteen factors related to topographical, hydrological, soil type, geological and environmental conditions were used and a multi-collinearity test of the landslide conditioning factors (LCFs) was performed. Using the natural break method (NBM) in a geographic information system (GIS), the LSMs generated by the RF, FLDA, and ADTree models were categorized into five classes, namely very low, low, medium, high and very high landslide susceptibility (LS) zones. The very high susceptibility zones cover 15.37% (ADTree), 16.10% (FLDA) and 11.36% (RF) of the total catchment area. The results of the different models (FLDA, RF, and ADTree) were explained and compared using the area under receiver operating characteristics (AUROC) curve, seed cell area index (SCAI), efficiency and true skill statistic (TSS). The accuracy of models was calculated considering both the training and validation data. The results revealed that the AUROC success rates are 0.89 (ADTree), 0.92 (FLDA) and 0.97 (RF) and predication rates are 0.82 (ADTree), 0.79 (FLDA) and 0.98 (RF), which justifies the approach and indicates a reasonably good landslide prediction. The results of the SCAI, efficiency and TSS methods showed that all models have an excellent modeling capability. In a comparison of the models, the RF model outperforms the boosted regression tree (BRT) and ADTree models. The results of the landslide susceptibility modeling could be useful for land-use planning and decision-makers, for managing and controlling the current and future landslides, as well as for the protection of society and the ecosystem. Full article

Gullying is a type of soil erosion that currently represents a major threat at the societal scale and will likely increase in the future. In Iran, soil erosion, and specifically gullying, is already causing significant distress to local economies by affecting agricultural productivity and infrastructure. Recognizing this threat has recently led the Iranian geomorphology community to focus on the problem across the whole country. This study is in line with other efforts where the optimal method to map gully-prone areas is sought by testing state-of-the-art machine learning tools. In this study, we compare the performance of three machine learning algorithms, namely Fisher’s linear discriminant analysis (FLDA), logistic model tree (LMT) and naïve Bayes tree (NBTree). We also introduce three novel ensemble models by combining the aforementioned base classifiers to the Random SubSpace (RS) meta-classifier namely RS-FLDA, RS-LMT and RS-NBTree. The area under the receiver operating characteristic (AUROC), true skill statistics (TSS) and kappa criteria are used for calibration (goodness-of-fit) and validation (prediction accuracy) datasets to compare the performance of the different algorithms. In addition to susceptibility mapping, we also study the association between gully erosion and a set of morphometric, hydrologic and thematic properties by adopting the evidential belief function (EBF). The results indicate that hydrology-related factors contribute the most to gully formation, which is also confirmed by the susceptibility patterns displayed by the RS-NBTree ensemble. The RS-NBTree is the model that outperforms the other five models, as indicated by the prediction accuracy (area under curve (AUC) = 0.898, Kappa = 0.748 and TSS = 0.697), and goodness-of-fit (AUC = 0.780, Kappa = 0.682 and TSS = 0.618). The analyses are performed with the same gully presence/absence balanced modeling design. Therefore, the differences in performance are dependent on the algorithm architecture. Overall, the EBF model can detect strong and reasonable dependencies towards gully-prone conditions. The RS-NBTree ensemble model performed significantly better than the others, suggesting greater flexibility towards unknown data, which may support the applications of these methods in transferable susceptibility models in areas that are potentially erodible but currently lack gully data. Full article

Fusarium head blight (FHB) is one of the diseases caused by fungal infection of winter wheat (Triticum aestivum), which is an important cause of wheat yield loss. It produces the deoxynivalenol (DON) toxin, which is harmful to human and animal health. In this paper, a total of 89 samples were collected from FHB endemic areas. The occurrence of FHB is completely natural in experimental fields. Non-imaging hyperspectral data were first processed by spectral standardization. Spectral features of the first-order derivatives, the spectral absorption features of the continuum removal, and vegetation indices were used to evaluate the ability to identify FHB. Then, the spectral feature sets, which were sensitive to FHB and have significant differences between classes, were extracted from the front, side, and erect angles of winter wheat ear, respectively. Finally, Fisher’s linear discriminant analysis (FLDA) for dimensionality reduction and a support vector machine (SVM) based on radical basis function (RBF) were used to construct an effective identification model for disease severity at front, side, and erect angles. Among selected features, the first-order derivative features of SDg/SDb and (SDg-SDb)/(SDg+SDb) were most dominant in the model produced for the three angles. The results show that: (1) the selected spectral features have great potential in detecting ears infected with FHB; (2) the accuracy of the FLDA model for the side, front, and erect angles was 77.1%, 85.7%, and 62.9%. The overall accuracy of the SVM (80.0%, 82.9%, 65.7%) was slightly better than FLDA, but the effect was not obvious; (3) LDA combined with SVM can effectively improve the overall accuracy, user’s accuracy, and producer’s accuracy of the model for the three angles. The over accuracy of the side (88.6%) was better than the front (85.7%), while the over accuracy of the erect angle was the lowest (68.6%). Full article

Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white radishes. A successive-projections algorithm (SPA) was applied with 10 wavelengths selected to distinguish defective radishes with black hearts from normal samples. Pearson linear correlation coefficients were calculated to further refine the set of wavelengths with 4 wavelengths determined. Four chemometric classifiers were developed for classification of normal and defective radishes, using 420, 10 and 4 wavelengths as input variables. The overall classifying accuracy based on the four classifiers were 95.6%–100%. The highest classification with 100% was obtained with a back propagation artificial neural network (BPANN) for both calibration and prediction using 420 and 10 wavelengths. Overall accuracies of 98.4% and 97.8% were obtained for calibration and prediction, respectively, with Fisher's linear discriminant analysis (FLDA) based on 4 wavelengths, and was better than the other three classifiers. This indicated that the developed hyperspectral transmittance imaging was suitable for black heart detection in white radishes with the optimal wavelengths, which has potential for fast on-line discrimination before food processing or reaching storage shelves. Full article