International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 05 | May 2024
p-ISSN: 2395-0072
www.irjet.net
Detection and Prediction of Diseases in Arecanut Plants Tejaswi R1, Rajesh M Mysoremath2, Pradhan D Prabhu3, Honey Jain4, Prof. Mahitha G5 1,2,3,4Student Department of Computer Science and Engineering PES University Bangalore, India 5Professor, Department of Computer Science and Engineering PES University Bangalore, India
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Abstract - Arecanut is one of the major crops in India.
Because of its higher market value, it is grown more in India and other subtropical regions. This plant is susceptible to a number of diseases that cause great losses to farmers. These diseases occur due to sudden changes in temperature and other climatic conditions; Early detection of the disease is very important. This work involves a focus on early and accurate disease detection to reduce losses for farmers. The proposed work uses RGB images as input and a Res-Net model. It then assigns learnable weights and biases to different objects in the image, then based on the results, it learns to distinguish between them. Global average pooling is used for image detection. Key Words: Arecanut, Res-Net (Residual Network), Global average pooling, RGB images.
1.INTRODUCTION Arecanut is a crop that is widely cultivated in India. Karnataka, Kerala, and Assam are the major states that produce Arecanut in India. Karnataka produces the largest quantity of Arecanut in India, with a total cultivation area of218,010 hectares and a production of 457.560 tones. Arecanut is mainly grown in the southern and coastal districts of India under assured irrigation. The crop thrives well in areas with a temperature range of 20-34°C and an annual rainfall of 2000-5000 mm. The crop is grown as a garden crop and is usually intercropped with coconut, cocoa,pepper, and other crops. The crop is used in various forms, such as raw, boiled, or roasted, and is consumed as a mouth freshener. It is also usedin Ayurvedic medicine for its medicinal properties. Since it has several importance in India and it is also a major commercial crop there are some challenges to cultivate this crop. In that challenge occurrence of Disease is a major challenge due to changes in temperature and climatic conditions.
1.1 Diseases 1. Koleroga/Mahali/Fruit Rot: This is a major disease of arecanut that causes serious losses. The Characteristic symptoms are rot and significant loss of immature fruits, scattered near the base of the tree. Infected seeds lose their luster and © 2024, IRJET
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natural green quality and therefore have a low market value. Disease is spread by strong winds and torrential rain. The severity, persistence and spread of fruit rot disease are related to rainfall patterns. The disease usually appears 15 to 20 days after the onset of regular monsoon rains and may last until the end of the rainy season. The pathogen is a fungus called Phytophthora palmivora.
2. Anabe Roga/Foot Rot: This disease is characterized by the rotting of the roots and the lower part of the stem. Affected trees exhibit stunted growth, yellowing of leaves, and premature fruit drop. Disease caused by the fungus Fusarium solani 3. Bud Rot: This disease is characterized by the rotting of the apical bud and the surrounding leaves. Affected trees exhibit stunted growth, yellowing of leaves, and premature fruit drop. The disease is caused by the fungus Phytophthora palmivora 4. Yellow Leaf Disease: This disease is characterized by Leaf tips of 2 or 3 outermost whorl leaves turn yellow. Brown necrotic streaks run parallel to the spreading leaf veins. The yellow color extends to the middle of the leaf blade. The tips of the leaves turn yellow and dry. At the advanced stage, all leaves turn yellow. The yellow color of the leaves is visible from October to December. Finally, the foliage falls, leaving the tree trunk bare. The ends of the roots turn black and gradually rot
2. LITERATURE SURVEY Dhanuja K C et.al [1] Proposed detection and identification of cau nodes at exceptional, fair and poor levels. The defective regions are segmented using DL algorithm and BPNN classifier based on image technique. A random sample of 48 excellent, 46 poor, and 49 poor images was used to test the device. The classification rates for Excellent, Good, and Poor ratings were 91.7%, 89.1%, and 91.8%, respectively. And an average accuracy level of 90.9 was achieved. With the CD camera, the algorithm can accurately and successfully locate locations and determine the consistency of areca nuts, but he could not examine the covered slides. ISO 9001:2008 Certified Journal
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