Turkish Journal of Physiotherapy and Rehabilitation; 32(2) ISSN 2651-4451 | e-ISSN 2651-446X www.turkjphysiotherrehabil.org 1683 AN EFFICIENT HAND-CRAFTED FEATURES WITH MACHINE LEARNINGBASED PLANT LEAF DISEASE DIAGNOSIS AND CLASSIFICATION MODEL 1 K. JAYAPRAKASH, 2 DR. S.P. BALAMURUGAN 1 Assistant Professor/Programmer, Department of Education, Annamalai University. 2 Assistant Professor / Programmer, Division of Computer and Information Science, Annamalai University. Email: 1 [email protected], 2 [email protected]ABSTRACT India loses 35% of the yearly crop productivity owing to plant diseases. Earlier plant disease detection using traditional methods or human experts is a complex and time-consuming process. Therefore, rapid and automated plant disease detection models are essential to meet the increasing demand for food productivity and quality. Presently, computer vision and image processing techniques find useful for plant disease detection and increase crop yield sustainably. Therefore, this paper attempts to propose an efficient hand- crafted feature with machine learning based plant leaf disease diagnosis and classification model. The proposed model uses a Gaussian filtering (GF) technique to preprocess the input image and boosts its quality. Besides, Grabcut based segmentation technique is utilized to identify the diseased portions in the plant leaves. Moreover, two feature extractors namely local binary patterns (LBP) and Scale Invariant Feature Transform (SIFT) models are applied as feature extractors. At last, multilayer perceptron (MLP) and random forest (RF) models are employed as the classifier models to allocate the proper class labels to the test plant leaf images. The performance of proposed method is assessed against a benchmark plant leaf disease dataset and the experimental outcomes show the promising efficiency of the proposed model over the recent methods interms of different measures. Keywords: Agriculture, Plant disease detection, Machine learning, Intelligent models, Image processing, Tomato leaf disease I. INTRODUCTION Agriculture is the major contributor to national income in some ofthe countries. Though farmers make significant efforts in choosing healthier seed of plants and create appropriate environment for developing plants, it is several diseases which affect plant resulting to distinct plant diseases. The plant pathogens like (Virus, fungi, and Bacteria diseases) are the major cause of plant diseases. Similarly, a few insects that fed on the portions of plants like (sucking insect pest), and plant nutrition’s like (absence of micro components) also, contain crucial impact on developing plants [1]. The major problem in the area of agriculture is that it should determine the early detection of plant diseases batches in earlier phase which makes for suitable time control to decrease the loss, minimalize production cost, and raise the income. A common method for detecting and recognize plant diseases is naked eye observation of specialists. As the timely and correct detection of diseases is highly significant, automated methods are required to seek accurate, fast, less expensive disease detection. Image processing techniques could satisfy the requirements. The image processing is utilized from agricultural applications to succeeded determinations: (1) for identifying diseased fruit, leaf, stem, (2) for measuring infected region, (3) for detecting shape of infected region, (4) for defining color of infected region, and (5) for defining shape and size of fruits. Currently, automated identification of plant diseases fascinates various scientists in distinct fields due to their major advantages in observing huge fields of crops. Therefore, automated recognition of the disease’s symptoms is attained after they arise on plant leaves. The automated recognition method is commonly comprising of 2 major steps. Initially, the plant leaf image is taken by digital camera. Next, the classification and detection of leaf
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Turkish Journal of Physiotherapy and Rehabilitation; 32(2)
ISSN 2651-4451 | e-ISSN 2651-446X
www.turkjphysiotherrehabil.org 1683
AN EFFICIENT HAND-CRAFTED FEATURES WITH MACHINE
LEARNINGBASED PLANT LEAF DISEASE DIAGNOSIS AND
CLASSIFICATION MODEL
1K. JAYAPRAKASH,
2DR. S.P. BALAMURUGAN
1Assistant Professor/Programmer, Department of Education, Annamalai University. 2Assistant Professor / Programmer, Division of Computer and Information Science, Annamalai
Turkish Journal of Physiotherapy and Rehabilitation; 32(2)
ISSN 2651-4451 | e-ISSN 2651-446X
www.turkjphysiotherrehabil.org 1695
INCEPTION V3 63.40
Fig. 13. Accuracy analysis of proposed method with existing techniques
V. CONCLUSION
This paper has proposed an efficient hand-crafted feature with ML based plant leaf disease diagnosis and
classification model. The proposed method employed theGF technique to preprocess the image and thereby
filtered the noise that exists in it. In addition, the Grabcut technique is applied for segmenting the diseased and
non-diseased portions in the plant leaf image. Besides, LBP and SIFT models are used for the extraction of
meaningful features which are essential for further examination. At last, the MLP and RF models are utilized to
classify the plant leaf images into normal and diseased ones. For examining the disease detection efficiency of the
proposed model, a set of simulations were performed on benchmark plant leaf disease dataset. The experimental
results demonstrated the promising results of presented method over the recent techniques interms of different
measures. As a part of future scope, the efficiency of the proposed method can be raised via deep learning
models.
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