Convolutional neural network  for maize leaf disease image classification

Abstract. Traditional methods for detecting maize leaf diseases (such as leaf blight, sooty blotch, brown spot, rust, and purple leaf sheaf) are typically labor-intensive and strongly subjective. With the aim of achieving high accuracy and efficiency in the identification of maize leaf diseases from digital imagery, this article proposes a novel multichannel convolutional neural network (MCNN). The MCNN is composed of an input layer, five convolutional layers, three subsampling layers, three fully connected layers, and an output layer. Using a method that imitates human visual behavior in video saliency detection, the first and second subsampling layers are connected directly with the first fully connected layer. In addition, the mixed modes of pooling and normalization methods, rectified linear units (ReLU), and dropout are introduced to prevent overfitting and gradient diffusion. The learning process corresponding to the network structure is also illustrated. At present, there are no large-scale images of maize leaf disease for use as experimental samples. To test the proposed MCNN, 10,820 RGB images containing five types of disease were collected from maize planting areas in Shandong Province, China. The original images could not be used directly in identification experiments because of noise and irrelevant regions. They were therefore denoised and segmented by homomorphic filtering and region of interest (ROI) segmentation to construct a standard database. A series of experiments on 8 GB graphics processing units (GPUs) showed that the MCNN could achieve an average accuracy of 92.31% and a high efficiency in the identification of maize leaf diseases. The multichannel design and the integration of different innovations proved to be helpful methods for boosting performance. Keywords: Artificial intelligence, Convolutional neural network, Deep learning, Image classification, Machine learning algorithms, Maize leaf disease.

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Implementation of Transfer Learning in the Convolutional Neural Network Algorithm for Identification of Potato Leaf Disease

Procedia of Engineering and Life Science ◽

10.21070/pels.v1i1.820 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Abdul Jalil Rozaqi ◽

Muhammad Rudyanto Arief ◽

Andi Sunyoto

Keyword(s):

Neural Network ◽

Image Processing ◽

Convolutional Neural Network ◽

Image Classification ◽

Transfer Learning ◽

Method Development ◽

Classification Model ◽

Learning Method ◽

Potato Leaf ◽

Leaf Disease

Potatoes are a plant that has many benefits for human life. The potato plant has a problem, namely a disease that attacks the leaves. Disease on potato leaves that is often encountered is early blight and late blight. Image processing is a method that can be used to assist farmers in identifying potato leaf disease by utilizing leaf images. Image processing method development has been done a lot, one of which is by using the Convolutional Neural Network (CNN) algorithm. The CNN method is a good image classification algorithm because its layer architecture can extract leaf image features in depth, however, determining a good CNN architectural model requires a lot of data. CNN architecture will become overfitting if it uses less data, where the classification model has high accuracy on training data but the accuracy becomes poor on test data or new data. This research utilizes the Transfer Learning method to avoid an overfit model when the data used is not ideal or too little. Transfer Learning is a method that uses the CNN architecture that has been trained by other data previously which is then used for image classification on the new data. The purpose of this research was to use the Transfer Learning method on CNN architecture to classify potato leaf images in identifying potato leaf disease. This research compares the Transfer Learning method used to find the best method. The results of the experiments in this research indicate that the Transfer Learning VGG-16 method has the best classification performance results, this method produces the highest accuracy value of 95%.

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Machine Learning for Detection of Palm Oil Leaf Disease Visually using Convolutional Neural Network Algorithm

JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING ◽

10.31289/jite.v4i2.4185 ◽

2021 ◽

Vol 4 (2) ◽

pp. 286-293

Author(s):

Asrianda Asrianda ◽

Hafizh Al Kautsar Aidilof ◽

Yoga Pangestu

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Machine Learning ◽

Computer Vision ◽

Object Detection ◽

Convolutional Neural Network ◽

Image Classification ◽

Palm Oil ◽

Leaf Disease ◽

Neural Network Algorithm

Artificial intelligence (AI) merupakan bidang ilmu pengetahuan yang saat ini menjadi isu yang menarik dan masih diteliti secara luas. Salah satu cabang dari pengembangan AI adalah computer vision yang di dalamnya terdapat topik pembahasan image classification dan object detection. Machine learning dapat dimanfaatkan di dalam bidang computer vision untuk melakukan object detection dan image classification, yaitu dengan menggunakan algoritma Convolutional Neural Network (CNN). CNN banyak digunakan pada penelitian terdahulu karena akurasinya yang tinggi. Pada penelitian ini, CNN digunakan untuk mendeteksi jenis penyakit daun tanaman kelapa sawit, dengan dataset sebanyak 60 gambar, dimana 50 diantaranya merupakan daun dengan 5 jenis penyakit berbeda, yaitu Curvularia sp, Cochliobolus carbonus, Capnodium sp, Drecshlera, dan defisiensi unsur hara. Sedangkan 10 sisanya merupakan gambar daun sehat. Hasilnya, CNN dapat mendeteksi penyakit daun kelapa sawit dengan akurasi yang dihasilkan mencapai 99%.

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