Plant Cultivation Imaging for Low-Cost Abnormal State Detection

The patent replaces expensive deep learning control devices with simple, low-cost image analysis algorithms that can be executed on basic processors. The solution uses straightforward image processing techniques rather than complex neural networks, achieving adequate accuracy without requiring sophisticated hardware or extensive training data infrastructure.

The patent substitutes complex computational systems (deep learning) with simpler optical and algorithmic approaches. By using basic image processing on captured photographs rather than complex deep learning models, the system achieves plant state identification with reduced device complexity and lower computational requirements.

The patent eliminates the need for extensive preliminary data collection and model training by using pre-configured simple image analysis algorithms. The system is ready to analyze plant images immediately upon installation, without requiring time-consuming data acquisition phases or model training periods that deep learning systems would need.

The patent skips the lengthy data acquisition and model training phases inherent in deep learning approaches. By using straightforward image processing algorithms that don't require extensive training data, the system rapidly identifies plant abnormal states without the time investment needed for deep learning model development and validation.

The patent employs inexpensive image analysis algorithms and basic processing hardware instead of costly deep learning infrastructure. The solution uses simple computational methods that can be implemented on low-cost devices, eliminating the need for expensive GPUs, large-scale data collection systems, and specialized technical infrastructure required by deep learning approaches.

The patent replaces expensive deep learning computational systems with simpler, more affordable image processing algorithms. This substitution reduces manufacturing costs by eliminating the need for high-performance computing hardware and complex software licenses while maintaining sufficient accuracy for plant health monitoring.

The patent integrates the photographing portion into the existing cabinet structure, allowing the same component to serve both as part of the enclosure and as the imaging device mount. This multi-functional design provides convenient plant monitoring while avoiding the addition of separate, complex external components.

The patent combines the photographing portion with the cabinet structure, merging the imaging function into the existing framework. This integration approach simplifies the overall device by eliminating separate mounting structures and reducing the number of discrete components, thereby improving ease of operation without significantly increasing complexity.