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Southern corn leaf blight (SCLB) seriously threatens corn production. The timely and accurate monitoring of SCLB conditions (e.g., detection during the asymptomatic stage and severity classification during the symptomatic stage) is valuable for precision agriculture, because the application of pesticides depends on disease conditions. Compared with time-consuming and laborious field surveys, spectroscopy is a promising tool for plant disease monitoring. The unique advantages of combining multiple spectral enhancement features for monitoring rice and wheat diseases have been recognized. However, physiological and biochemical differences between maize leaves and rice and wheat leaves, along with the specific spectral response of SCLB, are likely to affect the performance of combining multiple spectral enhancement features. In addition, similar previous studies have not combined spectral slope features, i.e., first-order spectral derivatives (FSDs), with spectral bands (SBs) and spectral indices (SIs) and wavelet features (WFs) to improve plant disease detection. Thus, the performance of a method that combines FSDs, WFs, SBs, and SIs for SCLB asymptomatic detection, symptomatic detection, and symptomatic severity classification should be evaluated further. Here, the utility of combining SBs, SIs, WFs, and FSDs was quantified and evaluated in the asymptomatic detection, symptomatic detection, and symptomatic severity classification of SCLB. Various forms of spectral enhancement features that were sensitive to SCLB infection from the asymptomatic stage to the severe stage were first identified and combined using the RELIEF-F and sequential floating forward selection algorithms on the basis of two independent inoculation experiments. Finally, SCLB asymptomatic detection, symptomatic detection, and symptomatic severity classification models were developed and evaluated using the support vector machine algorithm. Results showed that combining FSDs with SBs, SIs, and WFs achieved the best performance in SCLB spectroscopic monitoring. (1) SCLB asymptomatic detection and symptomatic detection were moderately improved, i.e., overall accuracy (OA) and macro F1 (MF1) improved by ~ 1% to 2%. The OA of SCLB asymptomatic detection was 87.1% with an MF1 of 0.87, and that of symptomatic detection was 93.1% with an MF1 of 0.93. (2) SCLB symptomatic severity classification was significantly improved, i.e., OA and MF1 improved by ~ 7%. The OA of severity classification was 81.8% with am MF1 of 0.82. This study demonstrated that the complementary relationships among SBs, SIs, WFs, and FSDs could effectively improve SCLB spectroscopic monitoring. The proposed method provides a novel tool for large-scale SCLB spectroscopic monitoring. It has broad implications for assisting management decisions (i.e., when and where to apply pesticides and how much to apply) in precision agriculture. [ABSTRACT FROM AUTHOR]

BACKGROUND: Spatial‐explicit weed information is critical for controlling weed infestation and reducing corn yield losses. The development of unmanned aerial vehicle (UAV)‐based remote sensing presents an unprecedented opportunity for efficient, timely weed mapping. Spectral, textural, and structural measurements have been used for weed mapping, whereas thermal measurements—for example, canopy temperature (CT)—were seldom considered and used. In this study, we quantified the optimal combination of spectral, textural, structural, and CT measurements based on different machine‐learning algorithms for weed mapping. RESULTS: CT improved weed‐mapping accuracies as complementary information for spectral, textural, and structural features (up to 5% and 0.051 improvements in overall accuracy [OA] and Marco‐F1, respectively). The fusion of textural, structural, and thermal features achieved the best performance in weed mapping (OA = 96.4%, Marco‐F1 = 0.964), followed by the fusion of structural and thermal features (OA = 93.6%, Marco‐F1 = 0.936). The Support Vector Machine‐based model achieved the best performance in weed mapping, with 3.5% and 7.1% improvements in OA and 0.036 and 0.071 in Marco‐F1 respectively, compared with the best models of Random Forest and Naïve Bayes Classifier. CONCLUSION: Thermal measurement can complement other types of remote‐sensing measurements and improve the weed‐mapping accuracy within the data‐fusion framework. Importantly, integrating textural, structural, and thermal features achieved the best performance for weed mapping. Our study provides a novel method for weed mapping using UAV‐based multisource remote sensing measurements, which is critical for ensuring crop production in precision agriculture. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]