Deep learning for sustainable agriculture: Weed classification model to optimize herbicide application

Abstract

Herbicides, chemical substances designed to eliminate weeds, find widespread use in agriculture to eradicate unwanted plants and enhance crop productivity, despite their adverse impacts on both human health and the environment. The study involves the construction of a neural network classifier employing a Convolutional Neural Network (CNN) through Keras to categorize images with corresponding labels. This research paper introduces two distinct neural networks: a basic neural network and a hybrid variant combining CNN with Keras. Both networks undergo training and testing, yielding an accuracy of 30% for the basic neural network, whereas the hybrid neural network achieves an impressive 97% accuracy. Consequently, this model significantly diminishes the need for herbicide spraying over crops such as fruits, vegetables, and sugarcane, aiming to safeguard humans, animals, birds, and the environment from the detrimental effects of harmful chemicals. Functioning as the elevated API within the TensorFlow framework, Keras furnishes a user-friendly and immensely efficient interface tailored to address machine learning (ML) challenges, particularly in the realm of contemporary deep learning. Encompassing all facets of the machine learning process, from data manipulation to fine-tuning hyper parameters to deployment, Keras was meticulously crafted to expedite rapid experimentation.