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Yuvaraj, N.
- A Survey on Leaf Disease Prediction Algorithms using Digital Image Processing
Authors
1 Department of Computer Science, KPR Institute of Engineering and Technology, Coimbatore-641407, Tamil Nadu, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 10 (2016), Pagination: 1-4Abstract
Objective: To investigate the plant leaf disease prediction algorithms that utilizes the digital image processing techniques in agricultural environments.
Findings: In digital image processing, the segmentation process of healthy and diseased tissue was mainly focused in order to detect and diagnose the plant leaf diseases accurately.Semi-automatic segmentation technique was mostly utilized among various segmentation methods, which was developed based on the grayscale histogram. However, the issue of accuracy in segmentation process was still not improved. In this paper, the leaf disease prediction algorithms are investigated briefly according to the digital image processing techniques and evaluated the performance effectiveness of different algorithms.
Results: In this paper, various segmentation algorithms are studied which are used to predict the leaf diseases through digital image processing techniques in terms of their merits and demerits to prove segmentation based on grayscale histogram is better than other segmentation techniques to predict the leaf diseases.
Application/Improvements: The finding of this study shows that the segmentation technique based on grayscale histogram is better than the other digital image processing techniques.
Keywords
Digital Image Processing, Plant Disease, Segmentation, Grayscale Histogram, Leaf Symptoms.Full Text
References
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- A Survey on Crop Yield Prediction Models
Authors
1 M.E. (Computer Science and Engineering), KPR Institute of Engineering and Technology, Arasur, Coimbatore, IN
2 Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 12 (2016), Pagination: 1-7Abstract
Objectives: To analysis various models to improve the prediction of crop yield production.
Methods: In this paper, there are different methods has been analyzed to predict the crop yield. The methods such as artificial neural network, Adaptive Neuro-Fuzzy inference System, Fuzzy Logic and Multi Linear Regression are analyzed to know the best methods for crop yield prediction. The prediction of crop yield varied by internal factors and external factors of crop an environment. The internal factors such as pesticides, water level, spacing and fertilizers and the external factors such as temperature, humidity, and rainfall. There are various models were developed to predict the crop yield prediction. This paper provides detailed information about the different models for crop yield prediction.
Findings: In this paper various models for crop yield prediction are compared through their parameters such as Root Mean Square Error (RMSE), R2,correlation coefficient and Mean Square Error (MSE) to prove Adaptive NeuroFuzzy Inference System (ANFIS)prediction model is better than other techniques.
Application/improvements: The findings of this work prove that the Adaptive Neuro Fuzzy Inference System (ANFIS) prediction model provides better result than other approaches.
Keywords
Crop Yield Prediction, Adaptive Neurofuzzy Inference System, Data Mining, Agriculture.Full Text
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