Nowadays, sensors begin to play an essential role in smart-agriculture practices. Spectroscopy and the ground-based sensors have inspired widespread interest in the field of weed detection. Most studies focused on detection under ideal conditions, such as indoor or under artificial lighting, and more studies in the actual field environment are needed to test the applicability of this sensor technology. Meanwhile, hyperspectral image data collected by imaging spectrometer often has hundreds of channels and, thus, are large in size and highly redundant in information. Therefore, a key element in this application is to perform dimensionality reduction and feature extraction. However, the processing of highly dimensional spectral imaging data has not been given due attention in recent studies. In this study, a field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed and used to discriminate carrot and three weed species (purslane, humifuse, and goosegrass) in the crop field. Dimensionality reduction was performed on the spectral data based on wavelet transform; the wavelet coefficients were extracted and used as the classification features in the weed detection model, and the results were compared with those obtained by using spectral bands as the classification feature. The classification features were selected using Wilks’ statistic-based stepwise selection, and the results of Fisher linear discriminant analysis (LDA) and the highly dimensional data processing-oriented support vector machine (SVM) were compared. The results indicated that multiclass discrimination among weeds or between crops and weeds can be achieved using a limited number of spectral bands (8 bands) with an overall classification accuracy of greater than 85%. When the number of spectral bands increased to 15, the classification accuracy was improved to greater than 90%; further increasing the number of bands did not significantly improve the accuracy. Bands in the red edge region of plant spectra had strong discriminant capability. In terms of classification features, wavelet coefficients outperformed raw spectral bands when there were a limited number of variables. However, the difference between the two was minimal when the number of variables increased to a certain level. Among different discrimination methods, SVM, which is capable of nonlinear classification, performed better.

Timely and accurate estimation of regional winter wheat yield is of great significance for safeguarding food security and promoting sustainable agricultural development. In recent years, deep learning has been widely applied in crop yield estimation due to its powerful capability in mining complex relationships. However, the irregular shapes of administrative regions pose challenges for integrating spatial data such as remote sensing into deep learning models. To address this issue, this study employed mean-based aggregation and histogram-based dimensionality reduction techniques to preprocess spatial data, including remote sensing and meteorological data, thereby generating sample sets suitable for deep learning models. This study identified the phenomenon of feature conflict when processing heterogeneous features in conventional Long Short-Term Memory (LSTM) models and proposed a TB-LSTM (Two-Branch LSTM) model to mitigate such conflicts. The impact of different input feature combinations on estimation accuracy was analyzed, and the model’s capability for early yield prediction was further evaluated. The results show that: (1) The proposed TB-LSTM model achieved superior performance (R²: 0.853, RMSE: 516.619 kg/ha) compared to the baseline LSTM (R²: 0.353–0.732; RMSE: 735.378–1126.062 kg/ha), confirming its efficiency in resolving feature conflict and better exploiting the yield estimation potential of remote sensing and meteorological data. (2) The integration of meteorological data, spectral reflectance, and vegetation indices proved essential for achieving optimal yield estimation accuracy. Meteorological data provided the most significant contribution, while spectral reflectance and vegetation indices offered complementary information that improved model robustness. When all three data types were utilized simultaneously, the TB-LSTM model achieved peak estimation accuracy (R²: 0.853; RMSE: 514.013 kg/ha; MAE: 380.563 kg/ha). (3) The TB-LSTM model demonstrated robust early prediction capability. Using data from the first 27 time phases (covering growth stages up to heading), it successfully predicted winter wheat yields 48 days before harvest with optimal precision (R²: 0.868; RMSE: 487.327 kg/ha; MAE: 361.353 kg/ha). This capability supports proactive decision-making and resource allocation in agricultural management.

Ecosystem degradation stands as one of the most formidable barriers to achieving global sustainable development goals