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Modernizing Traditional Methods of Farming using Farming Robot

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Document pages: 15 pages

Abstract: This paper pertains to the study of a prototype which modernizes the agricultural sector. It has the ability to perform basic operations such as irrigation activity and monitoring of plants frequently without much manual labor. In addition to the above-mentioned functionalities, the system is trained for detecting diseases in plants. Agriculture is an area of prime importance in the existence of humanity. It is a process of cultivating land and plants to provide food, fiber, medicines and other products to enhance the quality of life. It is considered to be the main pivoting point in the rise of our civilization. In the proposed system ROFAR, detection of plant disease is achieved with the help of image processing and machine learning methods. Prompt and accurate detection of plant diseases is crucial for the quality and yield of crops. Advanced diagnosis and intervention can lower the cost of plant diseases and trim down the use of unnecessary pesticides. Images of leaves of different plant species were gathered and feature extraction was performed. As a result, the system was able to classify the plants based on its ailments accurately. The ROFAR gathers the images of the plants for disease detection from the field and were given as input to Convolution Neural Network (CNN) which then classifies the images as healthy or infected. The proposed system ROFAR undergoes a training phase and a testing phase. The system is trained by providing various samples of the normal and diseased plants. On completion of training phase, the system can identify any new images of plants as healthy, late blight, viral or bacterial. The system also facilitates the moisture detection in the soil. With these functionalities, crops with better quality and yield can be obtained from the field.

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