Integrating path planning and image processing with UAVs for disease detection and yield estimation in indoor agriculture

Abstract

The integration of Unmanned Aerial Vehicles (UAVs) with Controlled Environment Agriculture (CEA) is examined in this thesis, with a particular emphasis on tackling the major obstacles associated with disease detection and yield estimation in indoor farming. In order to maximize indoor agricultural practices, the research intends to take advantage of the advanced capabilities of UAVs fitted with high-resolution sensors together with complex path planning and image processing algorithms. One of the main components of the system is the path planning module. The main task of the path planning module is to effectively guide UAVs through the small areas of indoor farms. This entails flying in the shortest path possible, avoiding obstructions like plant beds, walls and support beams, and making sure the entire greenhouse is covered. Similar to the Traveling Salesman Problem (TSP), the problem is formulated using graph theory and the objective is to find the shortest route that visits all important points. Numerous algorithms, including heuristic approaches like Christofides' Algorithm and Nearest Neighbor, exact methods like Branch and Bound, and metaheuristic strategies like Genetic Algorithms, Ant Colony Optimization, and Simulated Annealing, were assessed. The study came to the conclusion that it would be better to use a combination of heuristic and metaheuristic strategies rather than exact algorithms. They provided the optimum compromise between computational efficiency and solution quality, and they were implemented using Google's OR-Tools library. The path planning module was implemented by generating grid points by using the greenhouse layout and computing distance matrices. Later, the TSP was resolved by refining the early solutions using local search metaheuristics and a variety of first solution methodologies. Path Cheapest Arc, the approach that was selected for the metaheuristic comparisons, showed a consistent rate of path creation, which qualified it for more comparisons and practical deployment. Identifying and counting fruits in snapshots that the UAVs took was part of the yield estimation task in the image processing module. Yolov8, a single-stage detector, was chosen because of its ability to merge speed and precision, which makes it perfect for real-time applications. With a high precision and recall metrics, the YOLOv8s model was trained on a dataset of 8,479 photos that included six different fruit classes. A number of measures were used to assess the model's performance, and the results showed that it was robust and effectively learned, including the Precision-Confidence Curve, F1-Confidence Curve, Recall-Confidence Curve, and Precision-Recall Curve. The main goal of disease detection was to categorize plant leaf images in order to recognize disease symptoms. Latest architectures with great accuracy and computational efficiency, such as YOLOv8s-cls, were selected. A dataset of 18,741 photos, containing both healthy and unhealthy apple and grape leaves, was used to train the model. Confusion matrices and training loss graphs were used to evaluate the model's performance, and they verified the model's dependability and capacity to discriminate between various disease classifications and health states. The ROS and Gazebo platforms were used for system integration and simulation. The UAV platform included key sensors and control algorithms that were integrated with the virtual environment. It was based on the Kopterworx Eagle quadcopter. With this configuration, the control techniques may be thoroughly tested and improved without the hazards that come with actual flight operations. The ROS framework enabled smooth communication between the path planning and image processing components, facilitating modular and distributed system development. The Image Processing node provided real-time picture analysis and precise yield estimation and disease detection while the Path Planner node created effective flight pathways. The UAV was able to function as it would have in a real greenhouse given that to the simulation setup in Gazebo, which imitated a realistic indoor agricultural environment. Throughout the interior setup, the UAV moved steadily and smoothly, accurately following the created flight routes. Real-time processing of the UAV's camera's acquired visuals translated into annotated images that validated accurate yield estimations and accurate disease symptom identification. Through these simulations, the system's capacity to identify unhealthy plants and calculate yields was verified; despite a couple of discrepancies, it demonstrated great accuracy and dependability. The study's findings suggested that an integrated system of unmanned aerial vehicles equipped with innovative path planning and image processing algorithms could substantially improve indoor agriculture's sustainability and efficiency. The dynamic time limit function of the path planning module was essential in guaranteeing effective functioning in different greenhouse sizes. The complexity of the greenhouse arrangement and the quantity of grid points were taken into account by this function while adjusting the time limit dynamically. Through iteratively executing randomized tests for varying point counts, the function determined the point at which solution distances plateud. By minimizing needless delays for simpler layouts and giving enough computing time for complex instances, this adaptive technique allowed the system to maintain superior performance. In the meantime, the image processing module's strong performance indicators highlighted how well it worked in real-time applications. Reducing the dangers and costs associated with physical trials, scalable and cost-effective testing was made possible by the use of the ROS and Gazebo simulation platforms. Additionally, the fruit detection model, tested with real-world images, demonstrated robust performance by utilizing average color analysis to filter grape varieties and reduce false positives, even under challenging conditions. The disease classification model accurately detected and classified leaves, with expert validation recommended to confirm the results, especially under non-ideal conditions. In conclusion, this study showed how combining UAVs with novel technologies might help indoor agriculture overcome its problems with yield estimation and disease detection. In simulated situations, the suggested system demonstrated successful outcomes, demonstrating its capacity to optimize resource allocation, enhance crop management, and guarantee a steady supply of food. Future research should concentrate on scalability and implementation in real-world scenarios, field testing in various indoor farming configurations, and investigating the integration of more sophisticated sensors and enhanced UAV flight dynamics. These developments will improve the system's overall performance in revolutionizing indoor agriculture methods as well as its resilience and adaptability.