Weeds Detection for Agriculture Using Convolutional Neural Network (CNN) Algorithm for Sustainable Productivity

Abstract

This project aims to develop a weed detection prototype for agricultural settings using the Convolutional Neural Networks (CNN) algorithm. The project thoroughly analyses and optimises CNN hyperparameters to improve accuracy and efficiency, empowering efficient weed control practices. The potential of this algorithm in weed detection is immense, offering a promising future for sustainable productivity in agriculture. Adopting innovative and sustainable agricultural practices is essential for building a robust and productive agriculture sector that can meet future food demands while protecting the environment. The research then assesses how well the CNN model generalises to various agricultural environments that support multiple crop situations. The dataset comprises 360 images of weeds, broadleaf, maise plants, soil and cotton crops. The images underwent four preprocessing phases: image scaling, normalisation, filtering, and segmentation. The proposed model achieved an accuracy of 89.82% utilizing the Convolutional Neural Network (CNN) algorithm, with the dataset partitioned into 80% for training and 20% for testing. Furthermore, the model attained an F1 score of 88.08%, indicating a high degree of alignment between predicted positive instances and actual positive samples. In addition to technological innovations in agriculture, this CNN-based weed detection prototype is a reliable resource for agriculturalists. AI-driven weed detection optimizes resource use, ensuring that pesticides and herbicides are applied only where necessary, reducing chemical overuse. This is in line with the United Nation Sustainable Development Goal (SDG) No. 12.