Design and implementation of AI system for fast and reliable weed control

Research plan for PhD Research question The research question being asked here to solve is to design and implement a fast AI system for weed control. The goal is to create a reliable system which satisfies a few key requirements. - Real-time functionality - Quick and efficient weed detection among different types of crops - Isolate the region of interest to pinpoint the location better - Navigate the robots to the affected area and find the best possible method to eradicate the unwanted growth - Usable in different angles and circumstances There are additional requirements which does not come into question immediately but falls under additional tasks which act as catalysts for the system to be scalable and efficient. - Identifying the type of weed - Morphological properties of the weed - Augmented reality (3D modelling of the scenario leading to better identification and research) - Creating a mechanism for the weed to be removed using the hardware available - Identifying the level of contamination in the crop fields to determine the priority and acting on it There are a few optional additions that can be made that can further enhance the quality of agriculture while working on the research at hand. - Using the real-time image classification to detect visual inconsistencies in the crops to detect fungal or bacterial infections early - Identifying pests affected areas to avoid infestation and limiting chemical use Why is it important? The agriculture industry is very diverse across the globe. While a lot of the Asian countries rely on cheap labour and manpower to cultivate the land and maintain the crops, the western world relies more on scientific advancement and heavy machinery. Both parts are seemingly progressing and relying more and more on genetic development and usage of chemicals. The chemicals in use in these cases fall predominantly on the pesticide and herbicide portion. This requires tremendous amount of manpower to identify and apply these chemicals to the affected areas. The cheaper and quicker option would be the application of mass herbicide which opens the gates for pollution and degradation of soil quality. This can leave long-term impacts on the environment which can render the land unusable in the near future. The best solution in this case is to identify the affected areas using artificial intelligence and apply concentrated amounts of herbicide there to eradicate the weeds early and often so the impact on the environment in minimal. This is why an accurate method for detecting the location of the weeds and establishing an automated real-time method to eradicate them is necessary. Methodology Without extensively reviewing the current standards, the simple methodology,
Methodology Without extensively reviewing the current standards, the simple methodology for this project would be to proceed using a task division and isolation method. The methods works as separate standalone methods which function independently and relies on the inputs and outputs of the other methods. The main methods in this case would be the dataset preparation, segmentation, feature extraction and the classification methods. The datasets used in the project would be subject to the availability from the industrial partner (Small robot company). The datasets would be the key factors of the project as the angles and the types of crop would navigate the way to go forward with the other tasks. The preparation would follow the likely normalisation of the images and how the images would compare to real-time data. Making sure the data is as close to the actual application as possible. The dataset would also guide the way for the segmentation task to commence. The segmentation task would be a simple resizing task where the region of interest would be isolated from the complete image. T