Quality Assessment of Crops Through Disease Detection Using Machine Learning
DOI:
https://doi.org/10.26438/ijcse/v8i2.99102Keywords:
Machine Learning, Segmentation, Clustering, CNNAbstract
Agriculture plays an important role in our country, crops are considered to be vital as they are the source of energy to mankind. Due to environmental conditions, crops are getting affected with many diseases. Farmers are not able to detect these diseases at an early stage. Disease in a crop leads to low productivity. Thus, assessment of crop condition is vital. Quality assessment of crops deals with assessing the quality and minimizing the loss of crops. It provides the fundamental information for understanding the quality of the crops and its diseases. There are various Machine Learning algorithms for detection and classification of diseases. Use of machine learning algorithms like CNN not only yields better results but it is also a cost efficient solution and it analyzes the data from different aspects, and classifies it into one of the predefined set of classes. In machine learning, Convolutional Neural Networks are complex feed forward neural networks. CNNs are used for image classification and recognition because of its high accuracy. CNN follows a hierarchical model which works on building a network and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed. CNN outperforms most of the ML algorithms when it comes to image classification provided there are large number of images present in the dataset. The morphological features and properties like color, intensity and dimensions of the plant leaves are taken in to consideration for classification. Thus, detection of disease in early stage will be beneficial for farmer so that necessary actions can be taken.
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