Computer Science


https://sites.google.com/a/gauhati.ac.in/research/computer/#rdc30082019 Smartphone to measure chlorophyll

https://sites.google.com/a/gauhati.ac.in/research/computer/#rdc30082019 Classifying the soil with image processing
Smartphone to measure chlorophyll contents

Ridip Dev Choudhury and his collaborator show how to measure the chlorophyll content with a smartphone, which may be a potential quick cost-effective way. This research work is published in the Journal of King Saud University - Computer and Information Sciences (Elsevier).


Authors
Abstract
The chlorophyll of leaf can be determined using soil plant analysis development meter or spectrophometer by agriculture scientists, agriculture experts, and farmers. Usually, these methods are very costly and may not be available to all the farmers and experts. Low greenness of leaf indicates low photosynthesis in the plant and it creates many problems in the plant. This paper forwards a low-cost smartphone image-based digital chlorophyll meter to predict the chlorophyll of citrus leaf. The chlorophyll of citrus leaf is predicted using Linear Regression (LR) and Artificial Neural Network (ANN). Here, ANN provides more accuracy as compared to LR in citrus chlorophyll prediction. Both methods are validated with the actual chlorophyll of the citrus leaf. The proposed method can be used as a reasonable method for chlorophyll prediction of citrus.

https://sites.google.com/a/gauhati.ac.in/research/computer/#top 
 



Classifying soil through image processing

Ridip Dev Choudhury and Utpal Barman show how a low-cost imaging method can provide valuable information to the rural farmer in the agriculture sector. This research work is published in Information Processing in Agriculture.


Authors
Abstract
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, the authors 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 camera. 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, colour moments, colour 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.

Journal Reference
https://sites.google.com/a/gauhati.ac.in/research/computer/#top