Investigating Efficiency of Soil Classification System using Neural Network Models

Soil is a vital requirement for agricultural activities providing numerous functionalities restoring both abiotic and biotic materials. There are different types of soils, and each type of soil possesses distinctive characteristics and unique harvesting properties that impact agricultural development in various ways. Generally, farmers in the olden days used to analyse soil by looking at it visually while some prefer laboratory tests which are time-consuming and costly. Testing of soil is done to analyse the features and characteristics of the soil type, which results in selecting a suitable crop. This in turn results in increased food productivity which is very beneficial to farmers. Hence, to recognize the soil type an automatic soil identification model is proposed by implementing Deep Learning Techniques. It is used to classify the soil for crop recommendation by analysing accurate soil type. Different Convolution Neural Networks have been applied in the proposed model. They are VGG16, VGG19, InceptionV3 and ResNet50.Among all those techniques it is analysed that better results were obtained with ResNet50 having an accuracy of about 87% performing Multi-classification that is Black soil, Laterite Soil, Yellow Soil, Cinder soil & Peat soil.

@article{Rao2023,
  title     = {Investigating Efficiency of Soil Classification System using Neural Network Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Pappala Mohan Rao and Kunjam Nageswara Rao and Sitaratnam Gokuruboyina and Neeli Koti Siva Sai Priyanka},
  doi       = {10.14569/IJACSA.2023.0141111},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141111}
}