Nutrient contents are important for plants. Lack of macronutrients causes plant damage. Several macronutrient deficiencies exhibit similar visual characteristics that are difficult for ordinary farmers to identify. Collaboration between Computer Vision technology and IoT has become a nondestructive method for nutrient monitoring and control, included in the hydroponic system. Computer vision plays a role in processing plant image data based on specific characteristics. However, the analysis of one characteristic cannot represent plant health. In addition, knowing the percentage of macronutrient deficiencies is also needed to support precision agriculture systems. Therefore, we propose a Multi Layer Perceptron architecture that can perform multi-tasks, namely, identification and estimation. In addition, the optimal architecture will also be sought based on the characteristics of the combination of three features in the form of texture, color, and leaf shape. Based on analysis and design, our proposed model has a high potential for identifying and estimating macronutrient deficiency at the same time as well and can be applied to support precision agriculture in Indonesia.
Chili is a horticultural crop that has high economic value in Indonesia. The productivity level of chili in the country is not proportional to the level of consumption, one of the causes is malnutrition. Each plant requires different amounts of macronutrients and micronutrients to support plant growth and development. Chili plants that lack or excess macronutrients show different visual symptoms. Digital Image Processing is a non-destructive method that is useful for determining plant health conditions based on visual symptoms of chili leaves. The combination of digital image processing and learning methods such as the Support Vector Machine (SVM) helps classify the types of macronutrient deficiencies in order to obtain a nutrient solution. In this study, there are several stages in determining macronutrient deficiencies in chili plants, namely image acquisition, pre-processing, feature extraction, to classification using SVM with several kernels. Based on the experimental results in this study, the SVM method can help modern farmers to determine the health condition of plants non-destructively with 97.76% accuracy using a Polynomial kernel. Applying this system to an intelligent farming system is expected to support the realization of precision agriculture in Indonesia.
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