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.

By 2050, food consumption and agricultural water use will increase as a result of a global population that is projected to reach 9 billion people. To address this food and water security challenge, there has been increased attention towards the concept of sustainable agriculture, which has a broad aim of securing food and water resources while preserving the environment for future generations. An element of this is the use of precision agriculture, which is designed to provide the right inputs, at the right time and in the right place. In order to optimize nutrient application, water intakes, and the profitability of agricultural areas, it is necessary to improve our understating and predictability of agricultural systems at high spatio-temporal scales. The underlying goal of the research presented herein is to advance the monitoring of croplands and crop yield through high-resolution satellite data. In addressing this, we explore the utility of daily CubeSat imagery to produce the highest spatial resolution (3 m) estimates of leaf area index and crop water use ever retrieved from space, providing an enhanced capacity to provide new insights into precision agriculture. The novel insights on crop health and conditions derived from CubeSat data are combined with the predictive ability of crop models, with the aim of improving crop yield predictions. To explore the latter, a sensitivity analysis-linked Bayesian inference framework was developed, offering a tool for calibrating crop models while simultaneously quantifying the uncertainty in input parameters. The effect of integrating higher spatio-temporal resolution data in crop models was tested by developing an approach that assimilates CubeSat imagery into a crop model for early season yield prediction at the within-field scale. In addition to satellite data, the utility of even higher spatial resolution products from unmanned aerial vehicles was also examined in the last section of the thesis, where future research avenues are outlined. Here, an assessment of crop height is presented, which is linked to field biomass through the use of structure from motion techniques. These results offer further insights into small-scale field variabilities from an on-demand basis, and represent the cutting-edge of precision agricultural advances.

Traditional image processing solutions have been applied in the past to detect and count strawberries. These methods typically involve feature extraction followed by object detection using one or more features. Some object detection problems can be ambiguous as to what features are relevant and the solutions to many problems are only fully realized when the modern approach has been applied and tested, such as deep learning. In this work, we investigate the use of R-CNN for green strawberry detection. The object detection involves finding regions of interest (ROIs) in field images using the selective segmentation algorithm and inputting these regions into a pre-trained deep neural network (DNN) model. The convolutional neural networks VGG, MobileNet and ResNet were implemented to detect subtle differences between green strawberries and various background elements. Downscaling factors, intersection over union (IOU) thresholds and non-maxima suppression (NMS) values can be tweaked to increase recall and reduce false positives while data augmentation and negative hardminging can be used to increase the amount of input data. The state of the art model is sufficient in locating the green strawberries with an overall model accuracy of 74%. The R-CNN model can then be used for crop yield prediction to forecast the actual red strawberry count one week in advance with a 90% accuracy.

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This study identified marginal agricultural lands in the Lower Mississippi Alluvial Valley using crop yield predicting models. The Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were trained and validated using county-level crop yield data, climate data, soil properties, and Normalized Difference Vegetation Index (NDVI). The RFR model outperformed MLR model in estimating soybean and corn yields, with an index of agreement (d) of 0.98 and 0.96, Nash-Sutcliffe model efficiency (NSE) of 0.88 and 0.93, and root mean square error (RMSE) of 9.34% and 5.84%, respectively. Marginal agricultural lands were estimated to 26,366 hectares using cost and sales price in 2021 while they were estimated to 623,566 hectares using average cost and sales price from 2016 to 2021. The results provide valuable information for land use planners and farmers to update field crops and plan alternative land uses that can generate higher returns while conserving these marginal lands.

En agriculture, la météo est le principal facteur de variabilité d’une année sur l’autre. Cette thèse vise à construire des modèles statistiques à grande échelle qui estiment l’impact des conditions météorologiques sur les rendements agricoles. Le peu de données agricoles disponibles impose de construire des modèles simples avec peu de prédicteurs, et d’adapter les méthodes de sélection de modèles pour éviter le sur-apprentissage. Une grande attention a été portée sur la validation des modèles statistiques. Des réseaux de neurones et modèles à effets mixtes (montrant l’importance des spécificités locales) ont été comparés. Les estimations du rendement de maïs aux États-Unis en fin d’année ont montré que les informations de températures et de précipitations expliquent en moyenne 28% de la variabilité du rendement. Dans plusieurs états davantage météo-sensibles, ce score passe à près de 70%. Ces résultats sont cohérents avec de récentes études sur le sujet. Les prévisions du rendement au milieu de la saison de croissance du maïs sont possibles à partir de juillet : dès juillet, les informations météorologiques utilisées expliquent en moyenne 25% de la variabilité du rendement final aux États-Unis et près de 60% dans les états plus météo-sensibles comme la Virginie. Les régions du nord et du sud-est des États-Unis sont les moins bien prédites. Le rendements extrêmement faibles ont nécessité une méthode particulière de classification : avec seulement 4 prédicteurs météorologiques, 71% des rendements très faibles sont bien détectés en moyenne. L’impact du changement climatique sur les rendements jusqu’en 2060 a aussi été étudié : le modèle construit nous informe sur la rapidité d’évolution des rendements dans les différents cantons des États-Unis et localisent ceux qui seront le plus impactés. Pour les états les plus touchés (au sud et sur la côte Est), et à pratique agricole constante, le modèle prévoit des rendements près de deux fois plus faibles que ceux habituels, en 2060 sous le scénario RCP 4.5 du GIEC. Les états du nord seraient peu touchés. Les modèles statistiques construits peuvent aider à la gestion sur le cours terme (prévisions saisonnières) ou servent à quantifier la qualité des récoltes avant que ne soient faits les sondages post-récolte comme une aide à la surveillance (estimation en fin d’année). Les estimations pour les 50 prochaines années participent à anticiper les conséquences du changement climatique sur les rendements agricoles, pour définir des stratégies d’adaptation ou d’atténuation. La méthodologie utilisée dans cette thèse se généralise aisément à d’autres cultures et à d’autres régions du monde. / In agriculture, weather is the main factor of variability between two consecutive years. This thesis aims to build large-scale statistical models that estimate the impact of weather conditions on agricultural yields. The scarcity of available agricultural data makes it necessary to construct simple models with few predictors, and to adapt model selection methods to avoid overfitting. Careful validation of statistical models is a major concern of this thesis. Neural networks and mixed effects models are compared, showing the importance of local specificities. Estimates of US corn yield at the end of the year show that temperature and precipitation information account for an average of 28% of yield variability. In several more weather-sensitive states, this score increases to nearly 70%. These results are consistent with recent studies on the subject. Mid-season maize crop yield forecasts are possible from July: as of July, the meteorological information available accounts for an average of 25% of the variability in final yield in the United States and close to 60% in more weather-sensitive states like Virginia. The northern and southeastern regions of the United States are the least well predicted. Predicting years for which extremely low yields are encountered is an important task. We use a specific method of classification, and show that with only 4 weather predictors, 71% of the very low yields are well detected on average. The impact of climate change on yields up to 2060 is also studied: the model we build provides information on the speed of evolution of yields in different counties of the United States. This highlights areas that will be most affected. For the most affected states (south and east coast), and with constant agricultural practice, the model predicts yields nearly divided by two in 2060, under the IPCC RCP 4.5 scenario. The northern states would be less affected. The statistical models we build can help for management on the short-term (seasonal forecasts) or to quantify the quality of the harvests before post-harvest surveys, as an aid to the monitoring (estimate at the end of the year). Estimations for the next 50 years help to anticipate the consequences of climate change on agricultural yields, and to define adaptation or mitigation strategies. The methodology used in this thesis is easily generalized to other cultures and other regions of the world.