Hyperspectral Reflectance and Indices for Characterizing the Dynamics of Crop–Weed Competition for Water

Multimodal remote sensing application for weed competition time series analysis in maize farmland ecosystems

Although weeds cause serious harm to crops through competition for resources, they also have ecological functions. We need to study the change law of competition between crops and weeds, and achieve scientific farmland weed management under the premise of protecting weed biodiversity. In the research, we perform a competitive experiment in Harbin, China, in 2021, with five periods of maize as the study subjects. Comprehensive competition indices (CCI-A) based on maize phenotypes were used to describe the dynamic processes and results of weeds competition. The relation between in structural and biochemical information of maize and weed competitive intensity (Levels 1-5) at different periods and the effects on yield parameters were analyzed. The results showed that the differences of maize plant height, stalk thickness, and N and P elements among different competition levels (Levels 1-5) changed significantly with increasing competition time. This directly resulted in 10%, 31%, 35% and 53% decrease in maize yield; and 3%, 7%, 9% and 15% decrease in hundred grain weight. Compared to the conventional competition indices, CCI-A had better dispersion in the last four periods and was more suitable for quantifying the time-series response of competition. Then, multi-source remote sensing technologies are applied to reveal the temporal response of spectral and lidar information to community competition. The first-order derivatives of the spectra indicate that the red edge (RE) of competition stressed plots biased in short-wave direction in each period. With increasing competition time, RE of Levels 1-5 shifted towards the long wave direction as a whole. The coefficients of variation of canopy height model (CHM) indicate that weed competition had a significant effect on CHM. Finally, the deep learning model with multimodal data (Mul-3DCNN) is created to achieve a large range of CCI-A predictions for different periods, and achieves a prediction accuracy of R2 = 0.85 and RMSE = 0.095. Overall, this study use of CCI-A indices combined with multimodal temporal remote sensing imagery and DL to achieve large scale prediction of weed competitiveness in different periods of maize.

Ecophysiological Response of Vitis vinifera L. in an Urban Agrosystem: Preliminary Assessment of Genetic Variability

Urban agriculture is an emerging challenge. Identifying suitable agrosystems that allow for the multiple functions of urban agriculture represents a key issue for the reinforcement of the agricultural matrix in cities, with the aims of counteracting and adapting to climate change and providing economic and social benefits. This study aims to produce a preliminary assessment of the adaptability of Italian native and non-native Vitis vinifera L. cultivars to the stressors of an urban environment. The investigation was carried out on the grapevine collection of the Botanical Garden of Rome (“Vigneto Italia”). A total of 15 grapevine varieties were selected for the evaluation of leaf chlorophyll content, stomatal conductance, and chlorophyll fluorescence under abiotic conditions during the growing season of 2021. Spectral signatures were collected from mature leaves, and several vegetation indices (LWI, MCARI, and WBI) were calculated. Our preliminary results highlighted differences in the behavior of the grapevine cultivars. The native ones showed a medium-high level for leaf chlorophyll content (greater than 350 mol m−2), good photosynthetic efficiency (QY > 0.75), and optimal stomatal behavior under drought stress (200 > gs > 50 mmol H2O m−2 s−1). The data allowed for the classification of the tested genotypes based on their site-specific resistance and resilience to urban environmental conditions. The grapevine proved to be a biological system that is highly sensitive to climate variables, yet highly adaptable to limiting growing factors.

Hyperspectral remote sensing to assess weed competitiveness in maize farmland ecosystems

Weed competition causes serious economic losses to maize production. Timely and accurate assessment of pressure from competition is crucial for ecological weed management. In this work, we apply hyperspectral remote sensing (HRS) technology to conduct a competitive experiment in Harbin, China, in 2021, with 5-leaf maize as the study target. A weed competition assessment method that combines comprehensive competition indices (CCI) and deep learning is proposed. For the comprehensive competition assessment, the relationship between different weed competitive pressures (Levels 1-5) and changes in the structural and physiological information of maize was analyzed. The accumulative/transient competition indices CCI-A and CCI-T were designed for accurate quantification. The results showed that parameters such as plant height, stalk thickness and nutrient elements of maize decreased with increasing competition level. Parameters, such as stomatal conductance and transpiration rate, showed a fluctuating change of increasing and then decreasing with increasing competition level. Compared with the traditional relative competitive intensity (RCI), the standard deviation of CCI is 0.303 and 0.499. The dispersion effect of CCI is better and more suitable for quantifying the competition response. HRS images combined with 3D-CNN model were then applied to reveal the spectral response to different weed competition pressures (Levels 1-5) and to make early predictions of weed competition. The first-order derivative showed that the spectral reflectance exhibited significant differences at 520-525 nm peak, 570-655 nm trough, and near 700 nm red edge. For hyperspectral spatial-spectral features, the 3D-CNN model is proposed for prediction of competing indices CCI. In addition, the VIP method is used to select the characteristic wavelengths. The 3D-CNN model achieves a prediction accuracy of RMSE = 0.106 and 0.152 using 13 feature bands, which can accurately quantify the subtle changes in competition indices. Overall, this study shows that the combination of CCI and deep learning can provide a multivariate and comprehensive assessment of weed competition pressure.

In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data

China is one the largest maize (Zea mays L.) producer worldwide. Considering water deficit as one of the most important limiting factors for crop yield stability, remote sensing technology has been successfully used to monitor water relations in the soil–plant–atmosphere system through canopy and leaf reflectance, contributing to the better management of water under precision agriculture practices and the quantification of dynamic traits. This research was aimed to evaluate the relation between maize leaf water content (LWC) and ground-based and unoccupied aerial vehicle (UAV)-based hyperspectral data using the following approaches: (I) single wavelengths, (II) broadband reflectance and vegetation indices, (III) optimum hyperspectral vegetation indices (HVIs), and (IV) partial least squares regression (PLSR). A field experiment was undertaken at the Chinese Academy of Agricultural Sciences, Beijing, China, during the 2020 cropping season following a split plot model in a randomized complete block design with three blocks. Three maize varieties were subjected to three differential irrigation schedules. Leaf-based reflectance (400–2500 nm) was measured with a FieldSpec 4 spectroradiometer, and canopy-based reflectance (400–1000 nm) was collected with a Pika-L hyperspectral camera mounted on a UAV at three assessment days. Both sensors demonstrated similar shapes in the spectral response from the leaves and canopy, with differences in reflectance intensity across near-infrared wavelengths. Ground-based hyperspectral data outperformed UAV-based data for LWC monitoring, especially when using the full spectra (Vis–NIR–SWIR). The HVI and the PLSR models were demonstrated to be more suitable for LWC monitoring, with a higher HVI accuracy. The optimal band combinations for HVI were centered between 628 and 824 nm (R2 from 0.28 to 0.49) using the UAV-based sensor and were consistently located around 1431–1464 nm and 2115–2331 nm (R2 from 0.59 to 0.80) using the ground-based sensor on the three assessment days. The obtained results indicate the potential for the complementary use of ground-based and UAV-based hyperspectral data for maize LWC monitoring.

Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem

The present study aimed to investigate the possibility of using hyperspectral imaging data to identify the invasive and weed species in agrocenoses ecosystem. The most common weeds in grain agrocenoses, i.e., Ambrosia artemisiifolia L., Euphorbia seguieriana Neck., Atriplex tatarica L., Glycyrrhiza glabra L., Setaria pumila (Poir.) Roem. and Schult, served as objects. The population of weeds, especially Ambrosia artemisiifolia is invasive for the selected region of study. Therefore, the shooting of objects was carried out with a hyperspectral camera, Cubert UHD185, and the values of 100 spectral channels were obtained from hyperspectral images. The values of 80 vegetation indices (VIs) were calculated. The material was processed using mathematical statistics (analysis of variance, t-test) and search methods of data analysis (principal component analysis, decision tree, and random forest). Using statistical methods, the simultaneous use of several VIs differentiated between species more deliberately and precisely. The combination of VIs Derivative index (D1), Chlorophyll content index (Datt3), and Pigment specific normalized difference (PSND) can be used for weeds identification. Using the decision tree method, VIs established a good division of weeds into groups; (1) perennial rhizomatous weeds (Euphorbia seguieriana, and Glycyrrhiza glabra), and (2) annual weeds (A. artemisiifolia, A. tatarica, and S. pumila); These Vis are Chlorophyll index (CI), D1, and Datt3. Using the random forest method, the VIs that have the greatest impact on Mean Decrease Accuracy and Mean Decrease Gini are D1, Datt3, PSND, and Double Peak Index (DPI). The use of spectral channel values for the identification of plant species using the principal component analysis, decision tree, and random forest methods showed worse results than when using VIs. A great similarity of the results was obtained with the help of statistical and search methods of data analysis.

Fluorescence spectroscopy and multispectral imaging for fingerprinting of aflatoxin-B1 contaminated (Zea mays L.) seeds: a preliminary study

Cereal seeds safety may be compromised by the presence of toxic contaminants, such as aflatoxins. Besides being carcinogenic, they have other adverse health effects on humans and animals. In this preliminary study, we used two non-invasive optical techniques, optical fiber fluorescence spectroscopy and multispectral imaging (MSI), for discrimination of maize seeds naturally contaminated with aflatoxin B₁ (AFB₁) from the uncontaminated seeds. The AFB₁-contaminated seeds exhibited a red shift of the emission maximum position compared to the control samples. Using linear discrimination analysis to analyse fluorescence data, classification accuracy of 100% was obtained to discriminate uncontaminated and AFB₁-contaminated seeds. The MSI analysis combined with a normalized canonical discriminant analysis, provided spectral and spatial patterns of the analysed seeds. The AFB₁-contaminated seeds showed a 7.9 to 9.6-fold increase in the seed reflectance in the VIS region, and 10.4 and 12.2-fold increase in the NIR spectral region, compared with the uncontaminated seeds. Thus the MSI method classified successfully contaminated from uncontaminated seeds with high accuracy. The results may have an impact on development of spectroscopic non-invasive methods for detection of AFs presence in seeds, providing valuable information for the assessment of seed adulteration in the field of food forensics and food safety.

Remote and Proximal Assessment of Plant Traits

The inference of functional vegetation traits from remotely sensed signals is key to providing efficient information for multiple plant-based applications and to solve related problems [...]