1
Department of EEE,AMET Deemed to be University, Chennai,Tamil Nadu, India
2
Department of ECE, Indian Institute of Information Technology, Trichy
Abstract
Internet of Things and Artificial Intelligence has led to the advancements in smart agriculture to improve our country’s economy. This paper focusses on developing an efficient framework for automatic detection ofdiseases in plant leaves during the onset of diseases. Timely detection and accurate identification of diseases is helpful in preventing the spread of the diseases thereby reducing the crop damage significantly. A novel color component based segmentation process is proposed in this work. To evaluate the performance of the developed algorithm Python IDE was used and metrics such as detection accuracy and classification accuracy were used.
J, P., R, K., Sasilatha, D., & V, B. (2021). An Efficient Color Based Segmentation And Feature Extraction Framework For Leaf Disease Detection In Smart Agriculture. Int. J. of Aquatic Science, 12(3), 539-546.
MLA
Padmapriya. J; Karthickmanoj. R; Dr.T. Sasilatha; Bharathi. V. "An Efficient Color Based Segmentation And Feature Extraction Framework For Leaf Disease Detection In Smart Agriculture". Int. J. of Aquatic Science, 12, 3, 2021, 539-546.
HARVARD
J, P., R, K., Sasilatha, D., V, B. (2021). 'An Efficient Color Based Segmentation And Feature Extraction Framework For Leaf Disease Detection In Smart Agriculture', Int. J. of Aquatic Science, 12(3), pp. 539-546.
VANCOUVER
J, P., R, K., Sasilatha, D., V, B. An Efficient Color Based Segmentation And Feature Extraction Framework For Leaf Disease Detection In Smart Agriculture. Int. J. of Aquatic Science, 2021; 12(3): 539-546.
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