The leaf analysis in a crop can present the need of a nutrient determined in the plant. The macronutrients deficiency in the cotton crop can be identified by specific type of colors variation by leaves images. Early identification of macronutrients deficiency can help in the growing suitable of the crop and reduce the use of agricultural inputs. This study investigates the image segmentation of the cotton leaves with deficiency of the phosphor. The segmentation is performed by difference of leaf pigmentation, according with the pattern related to macronutrient type in deficit and the cultivate. The image segmentation is made by an artificial neural network and the Otsu method. The results show satisfactory values with an optimized artificial neural network and better than the Otsu method. The results are presented by images and distinct parameters of quality analysis in the segmentation.

Artificial Neural Networks are widely used in various applications in engineering, as such solutions of nonlinear problems. The implementation of this technique in reconfigurable devices is a great challenge to researchers by several factors, such as floating point precision, nonlinear activation function, performance and area used in FPGA. The contribution of this work is the approximation of a nonlinear function used in ANN, the popular hyperbolic tangent activation function. The system architecture is composed of several scenarios that provide a tradeoff of performance, precision and area used in FPGA. The results are compared in different scenarios and with current literature on error analysis, area and system performance.

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HighlightsIdentifying the deficiency of potassium macronutrients in the soybean crop using artificial neural network (ANN).Image processing based on ANN using a reconfigurable device.Processing and representation of data in neurons are in floating point.Abstract. Precision agriculture aims to improve the production of field crops using different techniques to manage the planting stages, such as the monitoring of field crops by images, fertilization control, nutrient analysis of the soil, and pest and weed control. By investigating field images, a plant leaf can be used to identify the lack of nutrients or the presence of diseases. This study developed a system that identifies the macronutrient deficiency of potassium in soybean crops by analyzing leaves. The methodology of this study was developed using different abstractions (Matlab and FPGA) to obtain consolidated results and facilitate low-level abstraction. The main contribution of this study is developing a multilayer artificial neural network system for a reconfigurable device. The developed system was applied in the image segmentation to determine potassium deficiency using soybean leaves and compared with a high-level abstraction system. The results of the reconfigurable device show that the mean hit percentages are 92%, 96%, and 95% in the leaf, trefoil, and field, respectively. The mean square error values were in the range of 10-2 and the quality factor was between 8.5 and 9.0. Keywords: Artificial Neural Networks, Digital image processing, Potassium deficiency, Reconfigurable device, Soybean.

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