Group: 2014-FYP-26



Dr. Muhammad Tahir


Weed control has been a troublesome issue since decades as they are the biggest enemy of crops, causing the yield loss of nearly 20-40% annually. In Pakistan, the farming communities mostly use cultural and chemical techniques, such as herbicides, for weed removal which may harm the actual crop as well as pollute the environment. The purpose of the project is to avoid these issues and develop an efficient automated system that can safely suppress the unwanted plants. The project involves two phases; weed identification and weed removal via closed loop control. Weed identification is done using OpenCV and python. For training the system, machine learning approach is used. Data set of tomato fields in initial growth stages is used. 250 image samples for plants and weeds, each, are taken. The data set is pre-processed to remove the noise and enhance the distinguishing features of plants. After this, geometrical, statistical, textural and shape-based features are computed and each sample is labeled manually either as a plant or weed. The labeled images are then used to train the SVM classifier model based on RBF kernel for better detection. To optimize the classifier model, principal component analysis is applied. The hardware mainly consists of a camera and a weeding tool mounted on a robot. Camera captures images of the field, and based on trained model, it detects weeds in the frame and find their x, y coordinates. The x-axis and z- axis motion is managed by stepper motors while y-axis motion is achieved by actuating gear motors attached with the wheels. Z-axis motion, covered by weeding tool actually kills the weed. So far, 76% accuracy has been achieved on 100 test images. Running time of the real-time detection algorithm is reduced by multi-threading.

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