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The objective of this study is to process the soil images to generate a digital soil classification system for rural farmers at low cost. Soil texture is the main factor to be considered before doing cultivation. It affects the crop selection and regulates the water transmission property. The conventional hydrometer method determines the percentage of sand, silt, and clay present in a soil sample. This method is very cost and time-consuming process. In this approach, we collect 50 soil samples from the different region of west Guwahati, Assam, India. The samples are photographed under a constant light condition using an Android mobile of 13 MP cameras. The fraction of sand, silt, and clay of the soil samples are determined using the hydrometer test. The result of the hydrometer test is processed with the United State Department of Agriculture soil classification triangle for the final soil classification. Soil images are processed through the different stages like pre-processing of soil images for image enhancement, extracting the region of interest for segmentation and the texture analysis for feature vector. The feature vector is calculated from the Hue, Saturation, and Value (HSV) histogram, color moments, color auto Correlogram, Gabor wavelets, and discrete wavelet transform. Finally, Support Vector Machine classifier is used to classify the soil images using linear kernel. The proposed method gives an average of 91.37% accuracy for all the soil samples and the result is nearly the same with the United State Department of Agriculture soil classification.

Experts help farmers to diagnose the citrus diseases by using agriculture laboratories or viewing the visual symptoms. These methods may not be accessible to all the farmers due to the expert’s cost and non-availability of laboratories. The proposed work presents the comparison of two different Convolutional Neural Network (CNN) architectures to classify diseases of the citrus leaf. In this paper, two types of CNN architectures, such as MobileNet and Self-Structured (SSCNN) classifiers were used to detect and classify citrus leaf diseases at the vegetative stage. The proposed work prepared a smartphone image based citrus disease dataset. Both the models were trained and tested on the same citrus dataset. The performances of the models were evaluated using the accuracy and loss of the training and validation sets, respectively. The best training accuracy of the MobileNet CNN was 98% with 92% validation accuracy at the epoch 10. But the best training accuracy of the SSCNN was 98% with 99% validation accuracy at the epoch 12. The proposed system indicates that the SSCNN is more helpful and accurate for smartphone image based citrus leaf disease classification. In addition, the SSCNN algorithm takes less computation time as compared to MobileNet and it can be considered a cost-effective method for citrus disease detection.