Detection of Plant Leaf Disease Using Image Processing and Automation of Pesticide Spraying

The plant diseases have a direct impact on the quality and quantity of the crop, and by diagnosing them, the market value of agricultural products increases. This exemplifies the significance of healthy plants as well as the relevance of early identification of disease on leaves. The early detection has the difficulty of manpower’s’ inadequate knowledge on usage of sophisticated technology for plant disease detection. To automate this time-consuming process, this work proposes to build a device that takes pictures as input and detects damaged leaves while classifying the plant condition. Based on the disease detected, suitable pesticide is suggested from the database and the automatic spraying in the affected part is carried out. In this article, diseases in tomato and potato plants using image processing and machine learning techniques (convolution neural network) are used to automate disease identification and automated method for suitable pesticide spraying which is implemented. The identification of the disease has been implemented with 96% accuracy. The hardware implementation has the wheels controlled by 12 V DC motor R365 connecting spraying machine nozzle. The L293D motor driver is controlled by Arduino Uno.