Hyperspectral imaging technology combined with deep forest model to identify frost-damaged rice seeds

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

“…Remotely Sensed Data and Vegetation Indices: Hyperspectral Images (Band 1 ∼ 4) [80], [81], C-Band Synthetic Aperture Radar (SAR) [40] Drones Based Data: High-resolution Images [37], [39], [87]- [92], [118], [119] Monitoring paddy rice growth Remotely Sensed Data and Vegetation Indices: LSWI [63],EVI [62], [63], NDVI [63], [66], [69], LAI [69], [71]- [73], C-Band Synthetic Aperture Radar (SAR) [83] Assessing quality of paddy rice Based on these spectral bands, several measurements can be derived and computed such as Land Surface Water Index (LSWI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) (see Table 2).…”

“…Hyperspectral images (Red, Blue, Green and Near Infrared One) can also be used to predict paddy rice yield estimation [75]- [77], assess the quality of paddy rice [69], [71], [72], [78], [79] and monitor paddy rice disease [80], [81].…”

“…CART is one of the variants of Decision Tree (DT) classifiers that can be used for classification or regression predictive modeling problems [57], [66], [146]. DT is one of important types of algorithm for supervised learning, particularly in predictive modeling [78], [81], [127]. DT are constructed via an algorithmic approach that optimizes the splitting of a data set based on different conditions of the data features.…”

“…For that reason, most of the researches used high-resolution images in monitoring the paddy rice disease [37], [39], [41], [87]- [92], [118]- [121] and hyperspectral images [80], [81] to detect and assess the paddy rice diseases. The ANN algorithm and its variants, CNN, are found to be very effective in classifying task for monitoring the paddy rice diseases [39]- [41], [81], [87], [88], [90], [119]. For instance, the ANN achieved better classification results compared to FC and SVM algorithms [39] and the calibrated CNN model still showed good classification ability in a small-scale sample set and it was selected as the best classification model compared to DT, k-NN and SVM [81].…”

Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning

“…16 However, for samples of small sizes such as rice seeds, most studies used the average spectra instead of spectral-spatial fusion data in the near infrared range as their information resource. 10,17 Therefore, it is necessary to devise other innovative measures to utilize both the spatial and spectral information of the comparatively small rice seeds in images to acquire much more robust and accurate results.…”