Disease Detection and Remote Monitoring in Chilli Crop Using Image Processing

Abstract: Observations today have verified that the average crop yield in India is declining due to illnesses that have affected fully grown plants. Chilli plant production is tough due to the plant's vulnerability to a variety of microorganisms, infectious illnesses, and pests. Infections in the chilli plant impact areas such as the leaves and stems. In the early stages of diagnosing chilli illnesses, leaf characteristics are examined. The leaf image is taken and analyzed to determine the health of the chilli plant. Pesticides are currently being tested on chilli plants on a regular basis without first determining the needs of each plant. This ensures that pesticides are only used when diseased plants are discovered. Keywords: Infections in the chilli plant, chilli illnesses, characteristics are examined, Pesticides are currently being tested on chilli plants.

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Analysis and Implementation of Fruit/Leave Disease Detection using Image Processing and Neural Approach

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst0709030 ◽

2021 ◽

Vol 7 (09) ◽

pp. 186-194

Author(s):

Krishna Madheshiya, Prashant Richhariya and Dr. Anita Soni

Keyword(s):

Image Processing ◽

Plant Disease ◽

Plant Diseases ◽

Disease Detection ◽

New Approach ◽

Detection Model ◽

Learning Framework ◽

Deep Cnn ◽

First Time ◽

Leaf Image

The latest generation of convolution neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of fruit/plant disease detection model, based on leaf image processing and classification, by the use of ANN. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training

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Pendeteksian Penyakit pada Daun Cabai dengan Menggunakan Metode Deep Learning

Jurnal Teknik Informatika dan Sistem Informasi ◽

10.28932/jutisi.v6i3.2857 ◽

2020 ◽

Vol 6 (3) ◽

Author(s):

Rosalina Rosalina ◽

Ardi Wijaya

Keyword(s):

Image Processing ◽

Deep Learning ◽

Detection System ◽

Raspberry Pi ◽

Disease Detection ◽

Crop Failure ◽

Detection Systems ◽

Desktop Application ◽

Percent Accuracy ◽

Leaf Image

Chili is one of the most essential horticultural plants in Indonesia. In addition to the lack of supply of plants, the price of chili on the market has increased dramatically. The shortage is affected by unpredictable climate changes, which have to result in many chili plants suffering from crop failure. It was because the disease infects chili plants so that harvests are decreased. This work would incorporate Deep Learning for image processing in Disease Detection Systems. This disease detection method will be used to help users, in particular chili farmers, identify whether or not the leaves of their chili plants are contaminated with the disease. This system would take a picture of chili leaf using a Raspberry Pi camera and implement image processing on the chili leaf image to collect valuable information on the image to find out whether or not the chili leaf is contaminated with the disease. The purpose of this research is to make a desktop application for a disease detection system that has the ability to detect whether or not a chili leaf is infected by several diseases, display the condition of the chili leaves, display the type of disease that infects the chili leaves (if any), and provide a percentage probability of the system in detecting the image of the chili leaves correctly (whether it is healthy chili leaves or sick chili leaves). The system reaches 100 percent accuracy with good brightness and distance less than 1 meter, while the system reaches 68.8 percent accuracy with poor brightness and distance greater than or equal to 100 percent. Keywords— chili leaf; deep learning; disease detection; raspberry pi

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