Food loss and waste: a carbon footprint too big to be ignored

Eight to ten percent of total global greenhouse gas emissions are associated with food loss and waste. Tackling the challenges of food loss and sustainable food waste management is key to fulfilling the Paris Agreement. However, among the Nationally Determined Contributions to the Paris Agreement, very few countries make references to food loss and waste. In this work, we reviewed the problem of food loss and waste from a global viewpoint and highlighted the opportunities of managing food loss and waste towards carbon mitigation and beyond. The importance of developing a coherent collaboration among all associated stakeholders was implied. Some recent policy developments and the impacts of COVID-19 pandemic are discussed followed by the summarization of potential solutions to tackling the fool loss and waste challenge

Intra-field Canopy Nitrogen Retrieval from Unmanned Aerial Vehicle Imagery for Wheat and Corn Crops in Ontario, Canada

The optimization of crop nitrogen fertilization to accurately predict and match the nitrogen (N) supply to the crop N demand is the subject of intense research due to the environmental and economic impact of N fertilization. Excess N could seep into the water supplies around the field and cause unnecessary spending by farmers. Understanding the detailed spatial information about a crop status is known as a farming management technique called precision agriculture, which allows farmers to maximize their yield and profit while reducing the inputs of fertilizers, pesticides, water, and insecticides. The goal of this study is to document and test the applicability and feasibility of using Unmanned Aerial Vehicle (UAV) to predict nitrogen weight of wheat and corn fields in south-west Ontario. This is investigated using various statistical modelling techniques to achieve the best accuracy. Machine learning techniques such as Random Forests and Support Vector Regression are used, which provide more robust models than traditional linear regression models. The results demonstrate that most spectral indices have a non-linear relationship with canopy nitrogen weight and show high degree of multicollinearity among the variables. In this thesis, the final nitrogen prediction maps of wheat and corn fields using UAV images and the derived models are provided

Intra-field Nitrogen Estimation for Wheat and Corn using Unmanned Aerial Vehicle-based and Satellite Multispectral Imagery, Plant Biophysical Variables, Field Properties, and Machine Learning Methods

Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to increase productivity, minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, PlanetScope satellite imagery, vegetation indices (VI), crop height, leaf area index (LAI), field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of corn and wheat fields in southwestern Ontario, Canada. Random Forests (RF) and Support Vector Regression (SVR) machine learning models were tested with combinations of variable datasets and evaluated for accuracy of canopy nitrogen weight prediction. The results demonstrate that UAV and satellite-based prediction models including spectral variables, crop biophysical parameters, and field conditions can provide accurate and useful information for fertilizer management

Mapping invasive plants using RPAS and remote sensing

The ability to accurately detect invasive plant species is integral in their management, treatment, and removal. This study focused on developing and evaluating RPAS-based methods for detecting invasive plant species using image analysis and machine learning and was conducted in two stages. First, supervised classification to identify the invasive yellow flag iris (Iris pseudacorus) was performed in a wetland environment using high-resolution raw imagery captured with an uncalibrated visible-light camera. Colour-thresholding, template matching, and de-speckling prior to training a random forest classifier are explored in terms of their benefits towards improving the resulting classification of YFI plants within each image. The impacts of feature selection prior to training are also explored. Results from this work demonstrate the importance of performing image processing and it was found that the application of colour thresholding and de-speckling prior to classification by a random forest classifier trained to identify patches of YFI using spectral and textural features provided the best results. Second, orthomosaicks generated from multispectral imagery were used to detect and predict the relative abundance of spotted knapweed (Centaurea maculosa) in a heterogeneous grassland ecosystem. Relative abundance was categorized in qualitative classes and validated through field-based plant species inventories. The method developed for this work, termed metapixel-based image analysis, segments orthomosaicks into a grid of metapixels for which grey-level co-occurrence matrix (GLCM)-based statistics can be computed as descriptive features. Using RPAS-acquired multispectral imagery and plant species inventories performed on 1m2 quadrats, a random forest classifier was trained to predict the qualitative degree of spotted knapweed ground-cover within each metapixel. Analysis of the performance of metapixel-based image analysis in this study suggests that feature optimization and the use of GLCM-based texture features are of critical importance for achieving an accurate classification. Additional work to further test the generalizability of the detection methods developed is recommended prior to deployment across multiple sites.remote sensingremotely piloted aircraft systemsRPASinvasive plant speciesmachine learnin

Multispectral in-field sensors observations to estimate corn leaf nitrogen concentration and grain yield using machine learning

Nitrogen (N) is the most critical fertilizer applied nutrient for supporting plant growth. It is a critical part of photosynthesis as a component of chlorophyl, hence it is a key indicator of plant health. In recent years, rapid development of multispectral sensing technology and machine learning (ML) methods make it possible to estimate leaf chemical components such as N for predicting yield spatially and temporally. The objectives of this study were to compare the relationships between canopy reflectance and corn (Zea mays L.) leaf N concentration acquired by two multispectral sensors: red-edge multispectral camera mounted on the Unmanned Aerial Vehicle (UAV) and crop circle ACS-430. Four fertilizer N rates were applied, ranging from deficient to excessivein order to have a broad rangein plant N status. Spectral information was collected at different phenological stages of corn to calculate vegetation indices (VIs) for each stage. Moreover, leaf samples were taken simultaneously to determine N concentration. Different ML methods (Multi-Layer Perceptron (MLP), Support Vector Machines (SVMs), Random Forest regression, Regularized regression models, and Gradient Boosting) were used to estimate leaf N% from VIs and predict yield from VIs. Random Forest Regression was utilized as a feature selection method to choose the best combination of variables for different stages and to interpret the relationships between VIs and corn leaf N concentration and grain yield. The Canopy Chlorophyll Content Index (SCCCI) and Red-edge Ratio Vegetation Index (RERVI) were selected as the most efficient VIs in leaf N estimation and SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were chosen as the most effective VIs in predicting corn grain yield. The results derived from using a red-edge multispectral camera showed that the SCCCI was the most proper index for predicting yield at most of the phenological stages and Gradient Boosting was the best-fitted model to estimate leaf N% with an 80% coefficient of determination. Using a Crop Circle ACS-430 showed that the Support Vector Regression (SVR) model achieved the best performance measures than other models tested in the prediction of leaf N concentration

Detecting and mapping forest nutrient deficiencies: eucalyptus variety (Eucalyptus grandis x and Eucalyptus urophylla) trees in KwaZulu-Natal, South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF