A survey of deep learning techniques for weed detection from images

Modern agricultural technology management is nowadays crucial in terms of the economy and the global market, while food safety, quality control, and environmentally friendly practices should not be neglected. This review aims to give perspectives on applying big data analytic and modern technologies to increase the efficacy and effectiveness of the coffee supply chain throughout the process. It was revealed that several tools such as wireless sensor networks, cloud computing, Internet of Things (IoT), image processing, convolutional neural networks (CNN), and remote sensing could be implemented in and used to improve the coffee supply chain. Those tools could help in reducing cost as well as time for entrepreneurs and create a reliable service for the customer. It can be summarized that in the long term, these modern technologies will be able to assist coffee business management and ensure the sustainable growth for the coffee industry.

https://www.mdpi.com/2071-1050/13/8/4593/pdf

Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

/pdf/weed-detection-using-deep-learning-a-systematic-literature-30rddqwj.pdf

Weed Detection Using Deep Learning: A Systematic Literature Review

High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

/pdf/resources-for-image-based-high-throughput-phenotyping-in-1iyd7h4m0y.pdf

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

Plants can be affected by various diseases. As such, the early detection of crop diseases plays an essential role in the farming industry. However, such detection requires extensive pathogen knowledge and is costly and labor-intensive. These challenges present an attractive opportunity to leverage machine learning (ML) and deep learning (DL) techniques to automate the detection of crop diseases. From this perspective, we present a review paper that showcases image-based plant disease detection and classification systems and discusses success stories using ML and DL techniques. In this review paper, we examine various aspects of these systems, including the sources of plant datasets, algorithm types and techniques used in ML and DL. The findings of this review paper inspire future research by highlighting the potential challenges in applying ML and DL to plant disease and pest detection. Additionally, it proposes potential solutions to overcome these challenges, paving the way for further advancements in developing and implementing automated systems for plant disease detection and classification. This work serves as a valuable resource for researchers and practitioners in the field, guiding their efforts toward more effective and accessible solutions for crop disease management. 

A Review on Machine Learning and Deep Learning Image-based Plant Disease Classification for Industrial Farming Systems

Human community are educated about the environmental issues of pesticides and fertilizers used in agriculture. There is a ever-growing demand for food to be met by agriculture producers. To reduce the environmental issues and address food security, IoT based precision agriculture has evolved. Precision agriculture not only reduces cost and waste, but also improves productivity and quality. We propose a system to detect and locate the weed plants among the cultivated farm crops based on the captured images of the farm. We also propose to enhance the performance of the above system using parallel processing in GPU such that it can be used in real-time. The proposed system takes real time image of farm as input for classification and detects the type and the location of weed in the image. The proposed work trains the system with images of crops and weeds under deep learning framework which includes feature extraction and classification. The results can be used by automated weed detection system under tasks in precision agriculture.

Weed Detection in Farm Crops using Parallel Image Processing

The IOT based online transformer monitoring records the electrical quantities such as load current, transformer oil, earth resistance, voltage, power factor, ambient temperatures. This online monitoring system integrates a Wi-Fi module (ESP8266), with stand alone Arduino microcontroller and sensor packages placed at site, those electrical quantities are stored in ADC and received parameters are processed and recorded in system memory, it sends the saved data to cloud, the cloud processing system gets data from microcontroller, and it integrates the details and represents the details in graphical manner. If there is any abnormality with transformer, graphical representation shows it in monitoring device. 

IoT-Based Smart Monitoring of online Transformer

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Weed Detection in Farm Crops using Parallel Image Processing

IoT-Based Smart Monitoring of online Transformer