This research uncovers a novel pathway in precision agriculture, emphasizing the utilization of advanced supervised and self-supervised deep learning approaches for an innovative solution to weed detection and crop yield prediction. The study focuses on key weed species: Italian ryegrass in wheat, Palmer amaranth, and common ragweed in soybean, which are troublesome weeds in the United States. One of the most innovative components of this research is the debut of a self-supervised learning approach specifically tailored for soybean yield prediction using only unlabeled RGB images. This novel strategy presents a departure from traditional yield prediction methods that consider multiple variables, thus offering a more streamlined and efficient methodology that presents a significant contribution to the field. To address the monitoring of Italian ryegrass in wheat cultivation, a bespoke Convolutional Neural Network (CNN) model was developed. It demonstrated impressive precision and recall rates of 100% and 97.5% respectively, in accurately classifying Italian ryegrass in the wheat. Among three hyperparameter tuning methods, Bayesian optimization emerges as the most efficient, delivering optimal results in just 10 iterations, contrasting with 723 and 304 iterations required for grid search and random search respectively. Further, this study examines the performance of various classification and object detection algorithms on Unmanned Aerial Systems (UAS)-acquired images at different growth stages of soybean and Palmer amaranth. Both the Vision Transformer and EfficientNetB0 models display promising test accuracies of 97.69% and 93.26% respectively. However, considering a balance between speed and accuracy, YOLOv6s emerged as the most suitable object detection model for real-time deployment, achieving an 82.6% mean average precision (mAP) at an average inference speed of 8.28 milliseconds. Furthermore, a self-supervised contrastive learning approach was introduced for automating the labeling of Palmer amaranth and soybean. This method achieved a notable 98.5% test accuracy, indicating the potential for cost-efficient data acquisition and labeling to advance precision agriculture research. A separate study was conducted to detect common ragweed in soybean crops and the prediction of soybean yield impacted by varying weed densities. The Vision Transformer and MLP-Mixer models achieve test accuracies of 97.95% and 96.92% for weed detection, with YOLOv6 outperforming YOLOv5, attaining an mAP of 81.5% at an average inference speed of 7.05 milliseconds. Self-supervised learning-based yield prediction models reach a coefficient of determination of up to 0.80 and a correlation coefficient of 0.88 between predicted and actual yield. In conclusion, this research elucidates the transformative potential of self-supervised and supervised deep learning techniques in revolutionizing weed detection and crop yield prediction practices. Its findings significantly contribute to precision agriculture, paving the way for efficient and cost-effective site-specific weed management strategies. This, in turn, promotes reduced environmental impact and enhances the economic sustainability of farming operations. / Master of Science in Life Sciences / This novel research provides a fresh approach to overcoming some of the biggest challenges in modern agriculture by leveraging the power of advanced artificial intelligence (AI) techniques. The study targets key disruptive weed species, such as, Italian ryegrass in wheat, Palmer amaranth, and common ragweed in soybean, all of which have the potential to significantly reduce crop yields. The studies were first conducted to detect Italian ryegrass in wheat crops, utilizing RGB images. A model is built using a complex AI system called a Convolutional Neural Network (CNN) to detect this weed with remarkable accuracy. The study then delves into the use of drones to take pictures of different growth stages of soybean and Palmer amaranth plants. These images were then analyzed by various AI models to assess their ability to accurately identify the plants. The results show some promising findings, with one model being quick and accurate enough to be potentially used in real-time applications. The most important part of this research is the application of self-supervised learning, which learns to label Palmer amaranth and soybean plants on its own. This novel method achieved impressive test accuracy, suggesting a future where data collection and labeling could be done more cost-effectively. In another related study, we detected common ragweed in soybean crops and predicted soybean yield based on various weed densities. AI models once again performed well for weed detection and yield prediction tasks, with self-supervised models showcasing high agreement between predicted and actual yields. In conclusion, this research showcases the exciting potential of self-teaching and supervised AI in transforming the way we detect weeds and predict crop yields. These findings could potentially lead to more efficient and cost-effective ways of managing weeds at specific sites. This could have a positive impact on the environment and improve the economic sustainability of farming operations, paving the way for a greener future.

From 2019 to 2020, greenhouse and field research was conducted at North Dakota State University to investigate the canopy temperature response of waterhemp (Amaranthus rudis), kochia (Kochia scoparia), common ragweed (Ambrosia artemisiifolia), horseweed (Conyza canadensis), Palmer amaranth (Amaranthus palmeri), and red root pigweed (Amaranthus retroflexus) after glyphosate application to identify glyphosate resistance. In these experiments, thermal images were captured of randomized glyphosate resistant populations and glyphosate susceptible populations of each weed species. The weed canopies' thermal values were extracted and submitted to statistical testing and various classifiers in an attempt to discriminate between resistant and susceptible populations. Glyphosate resistant horseweed, when collected within greenhouse conditions, was the only biotype reliably classified using significantly cooler temperature signatures than its susceptible counterpart. For field conditions, image based machine learning classifiers using thermal data were outperformed by classifiers made using additional multispectral data, suggesting thermal is not a reliable predictor of glyphosate resistance.

A identificação de plantas invasoras é de extrema importância em diversos procedimentos utilizados na agricultura. Apesar de ser uma tarefa computacionalmente difícil, esta identificação tem se tornado muito importante no contexto da agricultura de precisão. A agricultura de precisão substitui os tratos culturais de grandes áreas da cultura, feitos pela média do nível dos problemas encontrados nessas áreas, por tratamento específicos e pontuais. As pricipais vantagens são o aumento de produtividade, relacionado com a diminuição da variabilidade na produção, a economia de insumos e a preservação do meio ambiente. Este trabalho enfoca o reconhecimento de plantas invasoras em tempo real. Para manter o requisito de tempo real, são utilizadas redes neurais artificiais como meio para o reconhecimento de padrões. Entre as diversas plantas invasoras de ocorrência freqüente no cerrado brasileiro, foi selecionado o picão preto para a avaliação das técnicas adotadas. Uma arquitetura modular de reconhecimento é proposta, com o uso de processamento paralelo, facilitando a inclusão de módulos de reconhecimento de outras plantas invasoras sem a deterioração do desempenho do sistema. Os resultados obtidos são amplamente satisfatórios, demonstrando a possibilidade do desenvolvimento de um sistema embarcado completo de identificação de plantas invasoras em tempo real. Este sistema, apoiado pelo sistema de posicionamento global GPS, pode servir de base para uma série de máquinas agrícolas inteligentes, como pulverizadores de herbicidas e outros defensivos utilizados na agricultura. / Weed identification is an important task in many agricultural procedures. In spite of being a computation intensive task, this identification is very important in the role of precision agriculture. Conventional procedures in agriculture are based on the average level of the problems found in large areas. Precision agriculture introduces new punctual management procedures, dealing with very small areas. The main advantages are: productivity increase, related with the decrease in production unevenness, economy and environment preservation. This work focuses on the real time recognition of weeds. To maintain the real time requirement, neural networks are used to carry out the recognition of image patterns. Among the several weeds frequently found in the Brazilian savannah, the "picão preto" was selected for the evaluation of the adopted techniques. A modular architecture is proposed, using parallel processing, making easier the use of new recognition modules (for other weeds), still preserving the real time capabilities of the system. Results obtained are thoroughly adequate, demonstrating the possibility of the development of embedded systems for the identification of several weeds in real time. These systems, jointly with the global positioning system (GPS), can be used in a family of intelligent equipment, such as spraying machines for herbicides and other agricultural products.

A identificação de plantas invasoras é de extrema importância em diversos procedimentos utilizados na agricultura. Apesar de ser uma tarefa computacionalmente difícil, esta identificação tem se tornado muito importante no contexto da agricultura de precisão. A agricultura de precisão substitui os tratos culturais de grandes áreas da cultura, feitos pela média do nível dos problemas encontrados nessas áreas, por tratamento específicos e pontuais. As pricipais vantagens são o aumento de produtividade, relacionado com a diminuição da variabilidade na produção, a economia de insumos e a preservação do meio ambiente. Este trabalho enfoca o reconhecimento de plantas invasoras em tempo real. Para manter o requisito de tempo real, são utilizadas redes neurais artificiais como meio para o reconhecimento de padrões. Entre as diversas plantas invasoras de ocorrência freqüente no cerrado brasileiro, foi selecionado o picão preto para a avaliação das técnicas adotadas. Uma arquitetura modular de reconhecimento é proposta, com o uso de processamento paralelo, facilitando a inclusão de módulos de reconhecimento de outras plantas invasoras sem a deterioração do desempenho do sistema. Os resultados obtidos são amplamente satisfatórios, demonstrando a possibilidade do desenvolvimento de um sistema embarcado completo de identificação de plantas invasoras em tempo real. Este sistema, apoiado pelo sistema de posicionamento global GPS, pode servir de base para uma série de máquinas agrícolas inteligentes, como pulverizadores de herbicidas e outros defensivos utilizados na agricultura. / Weed identification is an important task in many agricultural procedures. In spite of being a computation intensive task, this identification is very important in the role of precision agriculture. Conventional procedures in agriculture are based on the average level of the problems found in large areas. Precision agriculture introduces new punctual management procedures, dealing with very small areas. The main advantages are: productivity increase, related with the decrease in production unevenness, economy and environment preservation. This work focuses on the real time recognition of weeds. To maintain the real time requirement, neural networks are used to carry out the recognition of image patterns. Among the several weeds frequently found in the Brazilian savannah, the "picão preto" was selected for the evaluation of the adopted techniques. A modular architecture is proposed, using parallel processing, making easier the use of new recognition modules (for other weeds), still preserving the real time capabilities of the system. Results obtained are thoroughly adequate, demonstrating the possibility of the development of embedded systems for the identification of several weeds in real time. These systems, jointly with the global positioning system (GPS), can be used in a family of intelligent equipment, such as spraying machines for herbicides and other agricultural products.