An Image-based Plant Weed Detector using Machine Learning

Plant diseases, pest infestation, weed pressure, and nutrient deficiencies are some of the grand challenges for the agricultural sector worldwide, which can result in substantial crop yield losses. To limit these losses, farmers must promptly identify the different types of plant weeds to stop their spread within agricultural fields. Farmers try to recognize plant weeds through color and multi-spectral imaging, and optical observation, which incorporates a significantly high degree of complexity, especially for large-scale farms. This paper presents an Artificial intelligence (AI)-powered system to automate the plant weeds identification process. The developed system uses the Convolutional Neural network (CNN) model as an underlying Machine Learning (ML) engine for classifying eight weed categories. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the suspected weed plants conveniently. It then displays the weed category along with the confidence percentage and classification time. The system is evaluated using different performance metrics, such as classification accuracy and processing time.