AI-POWERED WEED DETECTION SYSTEM FOR PRECISION FARMING

Abstract

In precision agriculture, where maximizing crop yield while minimizing resource usage is paramount, the need for efficient weed detection models is critical. Weeds pose a significant threat to crop productivity, competing for essential resources such as nutrients, water, and sunlight. Traditional methods of weed control, such as manual labor or blanket herbicide application, are time-consuming, labor-intensive, and often result in overuse of chemicals, leading to environmental degradation and economic inefficiency. This paper presents a Hyperparameter-Tuned Deep Learning model for Weed Detection and Classification (HPTDL-WDAC) suitable for Precision Farming applications. The proposed HPTDL-WDAC system integrates advanced techniques from computer vision and deep learning to accurately identify and classify weeds in agricultural fields. The workflow begins with pre-processing steps aimed at enhancing image quality and reducing noise. Specifically, a Gaussian Filter (GF) is employed to effectively remove noise from input images, followed by resizing to standard dimensions and class labelling for subsequent analysis.

For object detection and classification, the RetinaNet model is employed. RetinaNet's innovative architecture, featuring a focal loss mechanism, enables robust detection of weed instances amidst varying backgrounds and lighting conditions. Notably, the hyperparameters of the RetinaNet model are fine-tuned using the ADAM optimizer, optimizing its performance for the specific task of weed detection and classification in precision farming scenarios. A thorough simulation analysis of the HPTDL-WDAC technique was conducted using a benchmark dataset. Experimental results demonstrate the effectiveness of the proposed system in accurately detecting and classifying weeds in various agricultural systems. This shows that it exhibits improved results compared to recent approaches on various metrics.