Smartphone assisting convolutional neural networks for soil texture classification in dry and wet humid conditions in West Guwahati, Assam
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Abstract
Soil texture using a hydrometer or pipette method requires expertise, although these are accurate. A soil expert may help the farmer to detect the soil texture by analyzing the visual texture of the soil, which is not always accurate. This paper presents the smartphone image-based sand and clay soil classification in wet and dry humid conditions using Self Convolution Neural Network (SCNN) and finetuned MobileNet.A soil dataset of 576 soil images was prepared using a low-cost smartphone under natural light conditions. Different augmentation techniques such as shift, range, rotation, and zoom were applied to the soil dataset to increase the number of images in the soil dataset. The best performance of the MobileNet was reported at epoch 15 with a testing and training loss of 0.0091 and 0.0194, respectively. Though the SCNN model performed best at epoch 10 with a testing accuracy of 99.85%, the MobileNet reported less computation time (167.8s) than the SCNN (273.2s). The precision and recall of the models were 99.62 (MobileNet) and 99.84 (SCNN). The accuracy of the SCNN reported itself as the best model, whereas the computing time of the MobileNet reported itself as the best model in different humid conditions. The model can be used to replicate the traditional soil texture analysis method and the farmers can use it for better productivity.
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CNN, Image processing, MobileNet, Precision farming, Soil texture
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