Estimation of Dry Matter and N Nutrient Status of Choy Sum by Analyzing Canopy Images and Plant Height Information

The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height. A three-year experiment with different N levels (0, 25, 50, 100, 150, and 200 kg center dot ha(-1)) was conducted on Choy Sum. Variables of canopy coverage (CC) and plant height were used to build the dry matter and N estimation model. The results showed that the yields of N-0 and N-25 were significantly lower than those of high-N treatments (N-50, N-100, N-150, and N-200) for all three years. The variables of CC x Height had a significant linear relationship with dry matter, with R-2 values above 0.87. The good performance of the CC x Height-based model implied that the saturation problem of dry matter prediction was well-addressed. By contrast, the relationship between dry matter and CC was best fitted by an exponential function. CNDC models built based on CC x Height information could satisfactorily differentiate groups of N deficiency and N abundance treatments, implying their feasibility in diagnosing N status. N application rates of 50-100 kgN/ha are recommended as optimal for a good yield of Choy Sum production in the study region

Estimation of Botanical Composition in Mixed Clover-Grass Fields Using Machine Learning-Based Image Analysis

This study aims to provide an effective image analysis method for clover detection and botanical composition (BC) estimation in clover-grass mixture fields. Three transfer learning methods, namely, fine-tuned DeepLab V3+, SegNet, and fully convolutional network-8s (FCN-8s), were utilized to detect clover fractions (on an area basis). The detected clover fraction (CFdetected), together with auxiliary variables, viz., measured clover height (H-clover) and grass height (H-grass), were used to build multiple linear regression (MLR) and back propagation neural network (BPNN) models for BC estimation. A total of 347 clover-grass images were used to build the estimation model on clover fraction and BC. Of the 347 samples, 226 images were augmented to 904 images for training, 25 were selected for validation, and the remaining 96 samples were used as an independent dataset for testing. Testing results showed that the intersection-over-union (IoU) values based on the DeepLab V3+, SegNet, and FCN-8s were 0.73, 0.57, and 0.60, respectively. The root mean square error (RMSE) values for the three transfer learning methods were 8.5, 10.6, and 10.0%. Subsequently, models based on BPNN and MLR were built to estimate BC, by using either CFdetected only or CFdetected, grass height, and clover height all together. Results showed that BPNN was generally superior to MLR in terms of estimating BC. The BPNN model only using CFdetected had a RMSE of 8.7%. In contrast, the BPNN model using all three variables (CFdetected, H-clover, and H-grass) as inputs had an RMSE of 6.6%, implying that DeepLab V3+ together with BPNN can provide good estimation of BC and can offer a promising method for improving forage management

Estimation of Dry Matter and N Nutrient Status of Choy Sum by Analyzing Canopy Images and Plant Height Information

The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height. A three-year experiment with different N levels (0, 25, 50, 100, 150, and 200 kg∙ha−1) was conducted on Choy Sum. Variables of canopy coverage (CC) and plant height were used to build the dry matter and N estimation model. The results showed that the yields of N0 and N25 were significantly lower than those of high-N treatments (N50, N100, N150, and N200) for all three years. The variables of CC × Height had a significant linear relationship with dry matter, with R2 values above 0.87. The good performance of the CC × Height-based model implied that the saturation problem of dry matter prediction was well-addressed. By contrast, the relationship between dry matter and CC was best fitted by an exponential function. CNDC models built based on CC × Height information could satisfactorily differentiate groups of N deficiency and N abundance treatments, implying their feasibility in diagnosing N status. N application rates of 50–100 kgN/ha are recommended as optimal for a good yield of Choy Sum production in the study region

Facilitating Growth of Maize (Zea mays L.) by Biostimulants: A Perspective from the Interaction between Root Transcriptome and Rhizosphere Microbiome

The plant growth-promoting effects of biostimulants have been widely documented, while little is known about the intrinsic mechanism. In our study, a pot experiment was conducted to investigate the effects of biostimulants on maize, and the maize root transcriptome and rhizosphere microbiome were assessed. The physicochemical properties of the soil were significantly altered with various trends, and the growth and yield of maize were promoted by biostimulants. Sampling time and maize strain were the strongest factors that altered the rhizosphere microorganisms. Rhizosphere microbiota with biostimulant application exhibited high community robustness. Root transcriptome analysis suggested an altered expression profile induced by biostimulants and maize strains. An integrated correlation analysis demonstrated that phosphate and nitrate metabolism genes are tightly associated with some rhizosphere microbiota. These results implied the plant growth-promoting effects of biostimulants might act in a rhizosphere microorganism-dependent manner and help to expand the use of biostimulants in sustainable agriculture