dc.description.abstract | With the advancement of technology, its influence on people is becoming more and more profound. Whether it is in the areas of economy, transportation, quality of life, or entertainment, its presence is ubiquitous. The demand for high-quality fruits and vegetables is also increasing, which is causing agriculture and technology to gradually come closer together. With the use of professional techniques such as environmental control, quality analysis, and automatic harvesting, agricultural products can achieve higher quality and production efficiency.
"Smart agriculture" is the goal of humans to combine technology and agriculture, and automatic harvesting is one of the popular research areas. Automatic harvesting requires an object recognition system to detect the target. Traditional recognition systems in the past had high environmental requirements, and the environment would directly affect the success rate of image detection or analysis. Therefore, most researchers conducted studies in a controlled environment. AI image learning is currently one of the mainstreams of image recognition, and it is a branch of artificial intelligence that uses big data to train the system to recognize the characteristics of the target object. This enables the detection of objects in different environments to be considered, thereby significantly reducing the impact of the environment on recognition. Artificial intelligence is the mainstream of this generation and will also be an important chapter in human technology.
In this research, Image machine learning techniques were used to capture the location and coordinates of guava within the farm environment. Color analysis, masking, and other processing methods were employed to remove collision foam and detect areas of decay. To enhance the success rate of decay recognition analysis, methods such as white balance, object extraction, median filtering, and spatial color quantization were utilized to remove noise and restore colors, thus improving the identification of collision foam and decay features in the guava images.
In this experiment, a total of 400 guava images were used for testing. The success rate of the initial guava location identification was 61.34%. The subsequent color analysis masking achieved a success rate of 79.47%. Therefore, the overall success rate of guava position and decay recognition analysis in this experiment was 48.75%. | en_US |