Evaluation of convolutional neural networks for herbicide susceptibility-based weed detection in turf

Abstract

Deep learning methods for weed detection typically focus on distinguishing weed species, but a variety of weed species with comparable plant morphological characteristics may be found in turfgrass. Thus, it is difficult for deep learning models to detect and distinguish every weed species with high accuracy. Training convolutional neural networks for detecting weeds susceptible to herbicides can offer a new strategy for implementing site-specific weed detection in turf. DenseNet, EfficientNet-v2, and ResNet showed high F1 scores (≥0.986) and MCC values (≥0.984) to detect and distinguish the sub-images containing dollarweed, goosegrass, old world diamond-flower, purple nutsedge, or Virginia buttonweed growing in bermudagrass turf. However, they failed to reliably detect crabgrass and tropical signalgrass due to the similarity in plant morphology. When training the convolutional neural networks for detecting and distinguishing the sub-images containing weeds susceptible to ACCase-inhibitors, weeds susceptible to ALS-inhibitors, or weeds susceptible to synthetic auxin herbicides, all neural networks evaluated in this study achieved excellent F1 scores (≥0.995) and MCC values (≥0.994) in the validation and testing datasets. ResNet demonstrated the fastest inference rate and outperformed the other convolutional neural networks on detection efficiency, while the slow inference of EfficientNet-v2 may limit its potential applications. Grouping different weed species growing in turf according to their susceptibility to herbicides and detecting and distinguishing weeds by herbicide categories enables the implementation of herbicide susceptibility-based precision herbicide application. We conclude that the proposed method is an effective strategy for site-specific weed detection in turf, which can be employed in a smart sprayer to achieve precision herbicide spraying.

Detection and coverage estimation of purple nutsedge in turf with image classification neural networks

“…7,8 Both economic and environmental concerns have led to legal regulations regarding herbicide usage in several countries. 9,10 For instance, the European Union introduced measures for reducing herbicide applications and encouraged spotspraying in a dosage strictly necessary based on the degree of weed infestation. 7 Precision herbicide application refers to the intentional application of herbicides to specific areas where weeds are present, aiming to minimize herbicide usage and achieve effective weed control.…”

“…In Europe, the cost of herbicides represents around 40% of the total expense for all chemicals used in agriculture 7,8 . Both economic and environmental concerns have led to legal regulations regarding herbicide usage in several countries 9,10 . For instance, the European Union introduced measures for reducing herbicide applications and encouraged spot‐spraying in a dosage strictly necessary based on the degree of weed infestation 7 .…”

Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios

“…Currently, the most effective weed removal methods in turfgrasses or urban hard surfaces involve localized applications of nonselective biological products (i.e., acetic acid) [5] or thermal treatments [6], however, adequate efficacy has yet to be achieved. Robotic machines that can autonomously detect and remove weeds show great promise for more sustainable weed control in turfgrasses [7][8][9]. Weed detection is fulfilled using various methods such as image processing, machine learning, and computer vision techniques, and it's an area of active research and development.…”

A Combination of OBIA and Random Forest Based on Visible UAV Remote Sensing for Accurately Extracted Information about Weeds in Areas with Different Weed Densities in Farmland

“…China is the world's largest producer and consumer of rice, and rice production is tightly linked to the food security of 60% of the country's population. Weeds growing in rice fields compete with rice for water, nutrients, and growing space, resulting in a decline in rice yields and favorable conditions for the growth of rice viruses [1,2]. Asian sprangletop is one of the most harmful weeds, which grows fast and is widely distributed in all the provinces of China, so how to effectively control its growth has become a complex problem.…”