Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence

By Victor Partel, Sri Charan Kakarla, Y. Ampatzidis. Computers and Electronics in Agriculture, 2019

350 citations recorded by the source corpus.

Abstract Most conventional sprayers apply agrochemicals uniformly, despite the fact that distribution of weeds is typically patchy, resulting in wastage of valuable compounds, increased costs, crop damage risk, pest resistance to chemicals, environmental pollution and contamination of products. To reduce these negative impacts, a smart sprayer was designed and developed utilizing machine vision and artificial intelligence to distinguish target weeds from non-target objects (e.g. vegetable crops) and precisely spray on the desired target/location. Two different experimental scenarios were designed to simulate a vegetable field and to evaluate the smart sprayer’s performance. The first scenario contained artificial weeds (targets) and artificial plants (non-targets). The second and most challenging scenario contained real plants; portulaca weeds as targets, and sedge weeds and pepper plants as non-targets. Two different embedded graphics processing unit (GPU) were evaluated as the smart sprayer processing unit (for image processing and target detection). The more powerful GPU (NVIDIA GTX 1070 Ti) achieved an overall precision of 71% and recall of 78% (for plant detection and target spraying accuracy) on the most challenging scenario with real plants, and 91% accuracy and recall for the first scenario with artificial plants. The less powerful GPU (NVIDIA Jetson TX2) achieved an overall precision and recall of 90% and 89% respectively on the first scenario with artificial plants, and 59% and 44% respectively on the second scenario with real plants. Finally, an RTK GPS was connected to the smart sprayer and an algorithm was developed to automatically generate weed maps and visualize the collected data (after every application). This smart technology integrates a state of the art (AI-based) weed detection system, a novel fast and precision spraying system, and a weed mapping system. It can significantly reduce the quantity of agrochemicals required, especially compared with traditional broadcast sprayers that usually treat the entire field, resulting in unnecessary application to areas that do not require treatment. It could also reduce costs, risk of crop damage and excess herbicide residue, as well as potentially reduce environmental impact.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/faaea431613a029fc67051f94ad21d121620b127

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Plant Science

Citations in the corpus

Applications of Remote Sensing in Precision Agriculture: A Review (2020, background): “[255] developed a target weed sprayer using deep learning neural network approach for ground-sensor based weed detection that yielded in 71% application accuracy in experimental fields in Florida, USA.”
Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques (2019, background): “Ampatzidis and Partel (2019) developed a UAVbased high throughput phenotyping system for citrus crops utilizing machine learning and artificial intelligence.”
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments (2021, methodology): “A research utilized the UAV for the detection, classification, counting of trees, and evaluation of varieties of citrus by the implementation of a famous DL detection model named YOLO-v3 (Ampatzidis & Partel, 2019).”
A Survey of Deep Learning Techniques for Weed Detection from Images (2021, methodology): “Using three webcams on an ATV, Partel et al. (2019a) took videos and then converted them into different frames of images. In some occasions, it was necessary to change the image format to accurately train the model, especially when using public datasets. For instance, Binguitcha-Fare and Sharma (201”