An Efficient Crop Field Management Using Bi Directional LSTM Neural Network

The research in the detection of plant diseases using plant images based on machine learning is widely increased in the field of agriculture. This could be done with the images of infected rice (Oryza sativa L.) plants. The changes in atmospheric condition cause changes in soil condition and in temperature. Both air temperature and soil temperature have distinct roles in crops, which can also lead to diseases in rice plants. In this paper, a prototype is developed for the detection of rice plant diseases like bacterial leaf blight, brown spot, and leaf smut. The proposed prototype is developed by undergoing experiments on image processing using machine learning algorithms. Several images of rice leaf which are infected by diseases had been captured and preprocessed using a median filtering technique. The important features are extracted by using Discrete Wavelet Transform (DWT) for the diseased part of the leaf images. Then the green parts of the leaf have been removed, so as to extract the diseased part. The features are extracted based on the attributes like color, shape, and texture. For multiclass classification process, Adaptive Boosting support vector machine (AdaBoostSVM) Classifier is used. The proposed prototype results in the accuracy of about 98.8% in detecting and classifying the rice leaf disease.

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An Efficient Crop Field Management Using Bi Directional LSTM Neural Network

Cited by 2 publications

References 0 publications

Review of Deep Learning Architectures Used for Identification and Classification of Plant Leaf Diseases

Review of Deep Learning Architectures Used for Identification and Classification of Plant Leaf Diseases

Detection of rice plant disease using AdaBoostSVM classifier

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