Mango Leaf Disease Detection Using Ultrasonic Sensor

Abstract

Mango Plant Diseases wreak havoc on fruit production and cause growers to lose money. This dilemma prompted the development of a new technology for detecting and diagnosing mango plant illnesses. In agriculture, keeping an eye on the health and illness of crops is critical for the booming output of crops in the cultivation industry. A multilayer convolutional neural network (MCNN) is constructed for the classification of Mango leaves disease, which is a classic and cost-effective solution to the above problem. Canonical correlation analysis (CCA)-based fusion is used to extract and fuse the features. The use of an ultrasonic sensor to detect bacterial canker and phomba blight disease is proposed in this research. The ultrasonic sensor that produces a pulse reflected signal from mango leaves uses the echo pin. Microsoft Excel is used to record the pulse data. A threshold frequency for disease detection is calculated using these values. The proposed approach has a 90% accuracy rate.