Efficient Crop vs Weed Detection in Precision Agriculture: A CNN Approach for Real-Time Decision Making

Crop vs weed detection plays an important approach in the present day scenario, as it is more useful for the farmers as well as for cultivating healthier crops. Weed growth in between the crops reduces crop productivity and degrades agricultural land. There are many ways to control the growth of weeds such as herbicides spraying manually, etc. The earlier used techniques are not that much efficient in finding or eliminating the weeds. Such methods have a no of drawbacks, including increased time consumption and the potential for herbicide spraying to damage actual crops. As many methods are already used to solve this problem, we are introducing cutting-edge Convolutional Neural Network (CNN) architecture which strikes a harmonious balance between computational efficiency and model accuracy, using CNN helps in taking real-time decision-making and resource allocation in the field. The evaluation of our proposed CNN model is conducted on the available dataset, which helps in identifying and distinguishing between crops and weeds. The method that we are applying will contribute to the advancement of precision agriculture by giving an efficient computational solution, which is more helpful for the farmers and can be applied in the real-world farming scenario and agricultural land.

I. Introduction

The agricultural sector plays a crucial role in contributing to the Indian economy. A substantial portion of the Indian economy comes from the agriculture sector. Intelligent farm management, robotic precision agriculture, and automation in agriculture all depend on precise data regarding the field, the environment, the state, and the phenotype of individual plants. There are two main methods for detecting weeds: manual weed detection and technology-based weed detection.