Weed Detection in Agricultural Fields using Deep Learning Process (original) (raw)

Weed Detection Using Convolutional Neural Network

BOHR International Journal of Intelligent Instrumentation and Computing

Precision agriculture relies heavily on information technology, which also aids agronomists in their work. Weeds usually grow alongside crops, reducing the production of that crop. Weeds are controlled by herbicides. The pesticide may harm the crop as well if the type of weed isn’t identified. In order to control weeds on farms, it is required to identify and classify them. Convolutional Network or CNN, a deep learning-based computer vision technology, is used to evaluate images. A methodology is proposed to detect weed using convolutional neural networks. There were two primary phases in this proposed methodology. The first phase is image collection and labeling, in which the features for images to be labeled for the base images are extracted. In second phase to build the convolutional neural network model is constructed by 20 layers to detect the weed. CNN architecture has three layers namely convolutional layer, pooling layer and dense layer. The input image is given to convoluti...

Deep learning-based decision support system for weeds detection in wheat fields

International Journal of Electrical and Computer Engineering (IJECE), 2022

In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not effective (tillage, hand weeding, and hoeing and mowing). Therefore, using the deep learning based on convolutional neural network (CNN) will help to automatically identify weeds and then an intelligent system comes to achieve a localized spraying of the herbicides avoiding their large-scale use, preserving the environment. In this article we propose a smart system based on object detection models, implemented on a Raspberry, seek to identify the presence of relevant objects (weeds) in an area (wheat crop) in real time and classify those objects for decision support including spot spray with a chosen herbicide in accordance to the weed detected.

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

2021

Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pretrained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.

Classification of Weeds of Paddy Fields using Deep Learning

ECTI Transactions on Computer and Information Technology (ECTI-CIT)

Weed management is one of the important tasks in agriculture. Weeds in rice fields are usually managed using three ways - chemical herbicides, mechanical weeders, and manual weeding. Manual weeding becomes a problem when there is a shortage of agricultural laborers. Mechanical weeders are not suitable for direct-seeded rice fields. Chemical herbicides are not advisable especially when farmers do not know about site-specific weed management. Site-specific weed management is using the right herbicide in the right amount. Therefore, this paper investigates computer vision-based deep learning techniques with transfer learning classifying three types of weeds in paddy fields, namely sedges, grasses, and broadleaved weeds so that the right herbicide is recommended to the farmers. This would reduce the broadcast application and the overuse of the herbicides, thereby limiting the negative impact of the chemical herbicides on the environment. This research work shows promising results with a...

Deep Learning-Based Weed Identification for Precision Farming

2021

According to a recent study and analysis in agriculture, various factors influence crop yield. Weeds are the most significant threat to crop yield. Weed control is a worldwide issue that has received much coverage in recent years. This paper presents a method for developing a deep convolutional neural network (CNN) for weed identification based on the modified YOLO architecture with several pre-processing techniques. An image labeler using the Roboflow framework is used to locate the regions of interest as part of the image processing. We have used novel Mosaic data augmentation in this model to address the well-known "small object detection problem." To train the developed model, we created 3600 images with different sizes of weed. Sizes of YOLO anchor box were calculated from the training dataset using a k-means clustering approach. The model that resulted was tested on 10% of the images. We may justify that the established model could detect weed with an appropriate rec...

A Novel Approach for Identification of Weeds in Paddy By using Deep Learning Techniques

IJEER , 2022

Weed is an unwanted plant which is grown in agriculture land. The land which is not cultivated will be fully covered by Weeds. Management of weed is the major concern for farmer because the weed will reduce the crop production quantity. There are many methods to control the weeds, one of those methods is manual plucking which is expensive because it takes more time, consumes human work. Second is by applying any chemicals suggested by external experts. This may cause damage to the crop which is cultivated. Identifying weeds in early stage of crop growth and destroying them through proper method is most important for increasing the crop production. We proposed an efficient method for identifying and classifying weed in paddy field by using Deep learning-based computer vision techniques. We applied Semantic Segmentation model for classifying weeds in agriculture land. We trained our model with SegNet with different batch size of 16,32,64 and obtained a highest accuracy of 94.223 for dropout value 0.1 and batch size set to 32.

Convolutional Neural Networks for Use in Weed Detection

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022

Today, weed detection and plant detection in plants are increasingly challenging. Vegetable planting weeds have not received much attention thus far. Although the differences in weed species are significant, traditional methods for weed identification focused mostly on directly identifying weed. This work proposes an alternative approach that combines deep learning with image technologies. The dataset was initially trained using the CNN model. Once the training is finished, we can identify and predict whether the input image is a crop or a weed.

Weeds detection efficiency through different convolutional neural networks technology

International Journal of Electrical and Computer Engineering (IJECE), 2022

The preservation of the environment has become a priority and a subject that is receiving more and more attention. This is particularly important in the field of precision agriculture, where pesticide and herbicide use has become more controlled. In this study, we propose to evaluate the ability of the deep learning (DL) and convolutional neural network (CNNs) technology to detect weeds in several types of crops using a perspective and proximity images to enable localized and ultra-localized herbicide spraying in the region of Beni Mellal in Morocco. We studied the detection of weeds through six recent CNN known for their speed and precision, namely, VGGNet (16 and 19), GoogLeNet (Inception V3 and V4) and MobileNet (V1 and V2). The first experiment was performed with the CNNs architectures from scratch and the second experiment with their pre-trained versions. The results showed that Inception V4 achieved the highest precision with a rate of 99.41% and 99.51% on the mixed image sets and for its version from scratch and its pre-trained version respectively, and that MobileNet V2 was the fastest and lightest with its size of 14 MB.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Computer Systems Science and Engineering

Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique intends to properly discriminate the plants as well as weeds. The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine (ELM) based weed classification. The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization (FFO) algorithm. A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced outcomes over its recent approaches interms of several measures.

Deep learning application in diverse fields with plant weed detection as a case study

Proceedings of the International Conference on Artificial Intelligence and its Applications, 2021

Machine learning applications have gained popularity over the years as more advanced algorithms like the deep learning (DL) algorithm are being employed in signal identification, classification and detection of cracks or faults in structures. The DL algorithm has broader applications compared to other machine learning systems and it is a creative algorithm capable of processing data, creating pattern, interpreting information due to its high level of accuracy in pattern recognition under stochastic conditions. This research gives an exposition of DL in diverse areas of operations with a focus on plant weed detection which is inspired by the need to treat a specific class of weed with a particular herbicide. A Convolutional Neural Network (CNN) model was trained through transfer learning on a pre-trained ResNet50 model and the performance was evaluated using a random forest (RF) classifier, the trained model was deployed on a raspberry pi for prediction of the test data. Training accuracies of 99% and 93% were obtained for the CNN and RF classifier respectively. Some recommendations have been proffered to improve inference time such as the use of better embedded systems such as the Nvidia Jetson TX2, synchronizing DL hardware accelerators with appropriate optimization techniques. A prospect of this work would be to incorporate an embedded system, deployed with DL algorithms, on an