Evaluation of Image Segmentation and Filtering With ANN in the Papaya Leaf
This work addresses precision agriculture by providing a cheap monitoring method for macronutrient deficiency in papaya plants, but it appears incremental as it evaluates existing ANN methods on new data.
The paper tackled the problem of identifying plant nutrient deficiency symptoms in papaya leaves using image segmentation and filtering with artificial neural networks, achieving satisfactory segmentation results even under high noise levels.
Precision agriculture is area with lack of cheap technology. The refinement of the production system brings large advantages to the producer and the use of images makes the monitoring a more cheap methodology. Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In this paper is analyzed the method based on computational intelligence to work with image segmentation in the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for image segmentation and filtering, several variations of parameters and insertion impulsive noise were evaluated too. Satisfactory results are achieved with artificial neural for segmentation same with high noise levels.