for example, you detected weed on a large scale. What then? The problem is you are trying to solve a problem that doesn't exist. Detection + some automation (removing or other) would be a feasible path, I think

Hello Simranpreet Kaur , I want to give you a perspective of AI that perhaps you need to look relating to your question about scalability and computational constraints, robustness and environmental variability, and economic and infrastructure constraints.

Firstly, deploying AI-based weed detection systems across vast agricultural landscapes presents significant challenges in scalability. High-resolution imaging from drones, satellites, or robotic platforms generates massive datasets, requiring substantial computational power for real-time analysis. Edge computing can mitigate latency issues, but it demands sophisticated hardware integration, increasing operational costs. Therefore, AI models trained on small-scale datasets may struggle with generalization when applied to large, heterogeneous environments. Variability in weed species, crop types, soil conditions, and lighting further complicates model adaptation, necessitating continuous retraining, and dataset expansion, which are both resource-intensive.

Secondly, weed detection accuracy is highly dependent on environmental factors such as lighting conditions, soil moisture, plant phenology, and seasonal variations. Traditional AI models trained under controlled conditions may fail in real-world field applications due to shifts in weed morphology, occlusions from crops, or spectral variations caused by shadows and different soil types. Adverse weather conditions such as heavy rain, fog, or dust can degrade sensor performance, limiting the effectiveness of vision-based AI systems. Ensuring robust performance across diverse agroecosystems requires adaptive learning models that incorporate real-time field data, yet developing such self-improving AI frameworks remains a challenge.

Finally, the adoption of AI-driven weed detection in large-scale farming requires substantial investment in imaging hardware, data transmission infrastructure, and automation systems. Many farms, particularly in developing regions, lack the technological infrastructure to support AI deployment, including high-speed internet, cloud computing access, and advanced machinery. Moreover, integrating AI-based weed detection with existing precision agriculture systems demands interoperability across multiple platforms and compatibility with various agricultural machinery brands. The cost of maintenance, software updates, and retraining AI models further escalates operational expenses, posing a barrier to widespread adoption. Addressing these challenges necessitates cost-effective AI solutions, modular hardware designs, and scalable cloud-edge architectures tailored for diverse farming landscapes.

Of the three mentioned, when you dig deep into the challenge, you will be able to solve any of those challenges in a technical way; the following papers might be of use:

Article Using transfer learning-based plant disease classification a...

https://ieeexplore.ieee.org/abstract/document/10147225

Article Using a novel convolutional neural network for plant pests d...

https://www.nature.com/articles/s41598-024-82857-y

Dear Simranpreet Kaur ,

The integration of Artificial Intelligence (AI) into farming practices heralds a profound transformation in agriculture, promising unparalleled efficiency, sustainability, and productivity. By harnessing advanced technologies such as AI-powered precision agriculture, farmers gain unprecedented insights into their crops and land. Real-time data analysis from sensors, drones, and satellite imagery enables precise decision-making regarding irrigation, fertilization, and pest management. This targeted approach not only optimizes yields but also minimizes resource usage and environmental impact. Moreover, AI-driven robotics and automation systems revolutionize labour-intensive tasks, from planting to harvesting, enhancing operational efficiency and reducing dependency on manual labour. Predictive analytics and decision support systems empower farmers to anticipate challenges and make informed choices, ensuring resilience in the face of changing conditions. Despite challenges such as data privacy concerns and technological infrastructure limitations, the potential of AI to revolutionize farming practices and address global food security challenges is undeniable. With continued innovation and responsible deployment, AI promises to usher in a new era of smart, sustainable agriculture.

https://diversity.researchfloor.org/revolutionizing-and-agriculture-farming-through-artificial-intelligence/

Weed detection using machine learning is a critical aspect of agriculture, directly influencing crop yields and the overall productivity of farming systems. Traditionally, weed detection and removal have been labor-intensive and time-consuming tasks. However, with the advent of advanced technologies, particularly machine learning, the process of weed detection has seen significant improvements.

https://vocal.media/01/weed-detection-using-machine-learning-revolutionizing-agriculture

Regards,

Shafagat