Volume 20 No 20 (2022)
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Dynamic Target Constraint Spectral and Spatial Convolution Neural Network towards Soil Nutrient Classification and Prediction for Suitable high yield Crop Recommendation
S.Devidhanshrii1, Dr.R.Shanmugavadivu
Abstract
Soil nutrient analysis is significant process in the precision agriculture which assist farmer with suitable and efficient crop suggestion and recommendation towards potential yield by the cultivation throughout the growing seasons on basis of its soil fertility content. Further it prevents the major losses on the crop growth and productivity due to lack of nutrients. Nitrogen, prosperous and potassium are considered as primary nutrient of any soil type. In order to acquire the soil data for processing, hyperspectral imaging is used as effective mode as it is capable of discriminating the soil characteristics on basis of its spectral signatures. Hyperspectral imaging is a non –invasive technique and fast monitoring technique for accessing both conventional nutrient content of the soil and conventional nutrient content of the crop as hyperspectral images with large terrestrial data containing spectral reflectance value obtained using sensors through spectroscopy. On acquired hyperspectral images, computer vision technique is employed to carry out nutrient prediction and classification task. In order to carry out to those machine learning algorithm and deep learning model employed as conventional techniques. Despite of many advantageous such as better characterization and exploitation of the land surfaces by combining rich spectral and spatial information’s, it leads numerous challenges such as curse of dimensionality and high intraclass variability. To mitigate the above mentioned challenges, a new deep learning framework termed as Dynamic Target Constraint Spectral and Spatial Convolution Neural Network encompassing of endmember extraction, selection, classification and prediction processed on the various layer of the network. Initiallyhyperspectral image preprocessing is carried out to sharpening and smoothening of the spectral bands of the images. Preprocessed image is employed to sparseregression which is considered as spectral unmixing technique to extract the spectral characteristics of the different materials within the mixed pixels and recover the spectrum of the each pure spectral signature called end-member extraction. These extracted end members are employed to feature selection technique in order to build the representative spectral libraries and towards quantitatively selecting the subset of the spectra. It is further applied to the deep learning architectureto map multiple soil nutrient variables as feature map in the pooling and convolution layers. Further spectral distribution map helps to model the soil fertility index with nitrogen, potassium and prosperous contents and it classify the soil nutrients into classes on basis of the soil fertilityin activation using the spectral band value of the soil properties and fully connected layers and finally it predicts the suitable of the crop for cultivation in softmax layer. Experimental analysis of the proposed architecture has been evaluated on the Landsat-8 dataset which access the proposed performance of the dynamic target constrained convolution neural network framework on the available spectral indices of the soil contents against the existing deep learning based approaches. Proposed framework produces the 99.8% of the accuracy on classifying the soil nutrientparameters into fertile classes with respect to different spectral wavelength on the soil fertility index and it exhibits superior performance compared with other existing learning classification approaches
Keywords
Soil Nutrient classification, Convolution Neural Network, Spectral Reflectance Value, Hyperspectral Image, Endmembers extraction, Spectral Unmixing, Crop Prediction, Nitrogen Value, Chlorophyll Value, Soil Reaction pH
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