Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods.

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.

Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping.

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green-red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.

A novel recurrent neural network approach in forecasting short term solar irradiance.

Forecasting solar irradiance is of utmost importance in supplying renewable energy efficiently and timely. This paper aims to experiment five variants of recurrent neural networks (RNN), and develop effective and reliable 5-minute short term solar irradiance prediction models. The 5 RNN classes are long-short term memory (LSTM), gated recurrent unit (GRU), Simple RNN, bidirectional LSTM (Bi-LSTM), and bidirectional GRU (Bi-GRU); the first 3 classes are unidirectional and the last two are bidirectional RNN models. The 26 months data under consideration, exhibits extremely volatile weather conditions in Jinju city, South Korea. Therefore, after different experimental processes, 5 hyper-parameters were selected for each model cautiously. In each model, different levels of depth and width were tested; moreover, a 9-fold cross validation was applied to distinguish them against high variability in the seasonal time-series dataset. Generally the deeper architectures of the aforementioned models had significant outcomes; meanwhile, the Bi-LSTM and Bi-GRU provided more accurate predictions as compared to the unidirectional ones. The Bi-GRU model provided the lowest RMSE and highest R2 values of 46.1 and 0.958; additionally, it required 5.25*10-5 seconds per trainable parameter per epoch, the lowest incurred computational cost among the mentioned models. All 5 models performed differently over the four seasons in the 9-fold cross validation test. On average, the bidirectional RNNs and the simple RNN model showed high robustness with less data and high temporal data variability; although, the stronger architectures of the bidirectional models, deems their results more reliable.

Influence of aeration rate and reactor shape on the composting of poultry manure and sawdust.

To achieve successful composting, all the biological, chemical, and physical characteristics need to be considered. The investigation of our study was based on various physicochemical properties, i.e., temperature, ammonia concentration, carbon dioxide concentration, pH, electrical conductivity (EC), carbon/nitrogen (C/N) ratio, organic matter (OM) content, moisture content, bacterial population, and seed germination index (GI), during the composting of poultry manure and sawdust for different aeration rates and reactor shapes. Three cylindrical-shaped and three rectangular-shaped pilot-scale 60-L composting reactors were used in this study, with aeration rates of 0.3 (low), 0.6 (medium), and 0.9 (high) L min-1 kg-1 DM (dry matter). All parameters were monitored over 21 days of composting. Results showed that the low aeration rate (0.3 L min-1 kg-1 DM) corresponded to a higher and longer thermophilic phase than did the high aeration rate (0.9 L min-1 kg-1 DM). Ammonia and carbon dioxide volatilization were directly related to the temperature profile of the substrate, with significant differences between the low and high aeration rates during weeks 2 and 3 of composting but no significant difference observed during week 1. At the end of our study, the final values of pH, EC, moisture content, C/N ratio, and organic matter in all compost reactors were lower than those at the start. The growth rates of mesophilic and thermophilic bacteria were directly correlated with mesophilic and thermophilic conditions of the compost. The final GI of the cylindrical reactor with an airflow rate of 0.3 L min-1 kg-1 DM was 82.3%, whereas the GIs of the other compost reactors were below 80%. In this study, compost of a cylindrical reactor with a low aeration rate (0.3 L min-1 kg-1 DM) was more stable and mature than the other reactors. Implications: The poultry industry is growing in South Korea, but there are problems associated with the management of poultry manure, and composting is one solution that could be valuable for crops and forage if managed properly. For high-quality composting, the aeration rate in different reactor shapes must be considered. The objective of this study was to investigate various physicochemical properties with different aeration rates and rector shapes. Results showed that aeration rate of 0.3 L min-1 kg-1 DM in a cylindrical reactor provides better condition for maturation of compost.