Details

Autor(en) / Beteiligte

• Khamparia, Aditya
• Saini, Gurinder
• Gupta, Deepak
• Khanna, Ashish
• Tiwari, Shrasti
• de Albuquerque, Victor Hugo C.

Titel

Seasonal Crops Disease Prediction and Classification Using Deep Convolutional Encoder Network

Ist Teil von

• Circuits, systems, and signal processing, 2020-02, Vol.39 (2), p.818-836

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2020

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Agriculture plays a significant role in the growth and development of any nation’s economy. But, the emergence of several crop-related diseases affects the productivity in the agriculture sector. To cope up this issue and to make aware the farmers to prevent the expansion of diseases in crops and to implement effective management, crop disease diagnosis plays its significant role. Researchers had already used many techniques for this purpose, but some vision-related techniques are yet to be explored. Commonly used techniques are support vector machine, k -means clustering, radial basis functions, genetic algorithm, image processing techniques like filtering and segmentation, deep structured learning techniques like convolutional neural network. We have designed a hybrid approach for detection of crop leaf diseases using the combination of convolutional neural networks and autoencoders. This research paper provides a novel technique to detect crop diseases with the help of convolutional encoder networks using crop leaf images. We have obtained our result over a 900-image dataset, out of which 600 constitute the training set and 300 test set. We have considered 3 crops and 5 kinds of crop diseases. The proposed network was trained in such a way that it can distinguish the crop disease using the leaf images. Different convolution filters like 2 × 2 and 3 × 3 are used in proposed work. It was observed that the proposed architecture achieved variation in accuracy for the different number of epochs and for different convolution filter size. We reached 97.50% accuracy for 2 × 2 convolution filter size in 100 epochs, while 100% accuracy for 3 × 3 filter size which is better than other conventional methods.