Search

Recently, precision weed management has emerged as a promising solution for reducing the use of herbicides which is hazardous to crops and human health. Thus, accurate identification of weed in the early stage is the urge of current agricultural practices. Despite recent progress, developing an efficient weed identification system for real field scenarios is a serious challenge. To overcome this, a number of deep learning-based methods have been introduced in the literature. However, these methods require large volume of annotated data images which is rarely available. To address this gap, in this paper, a novel WeedGan method has been introduced for generating realistic synthetic images. The proposed WeedGan adopted concept of federated learning to reduce the computational load by introducing two discriminators. Additionally, a new loss function is defined for the efficient training of the generator. Extensive experiments have been performed to validate the performance of the WeedGan. First, the quality of images generated by the WeedGan was validated on cotton weed dataset in terms of FID score, and discriminator accuracy. The results are compared against four other state-of-the-art GAN models namely, DC-GAN, W-GAN, Info-Gan, and VIT-GAN. Further, classifiers performance of the generated dataset was evaluated using seven state-of-the-art transfer learning-based methods on the original, basic augmented, and WeedGan augmented datasets. The experimental results demonstrate that the proposed WeedGan has outperformed all the considered methods by achieving FID score of 282.76. Moreover, the classification performance of WeedGan augmented dataset was recorded highest with 97.82% on testing and 99.87% training accuracy with DenseNet121. [ABSTRACT FROM AUTHOR]

Potato tuber is crucial as a primary vegetable food crop across the globe. However, its quality and quantity are constantly threatened by fungal blight diseases, particularly late and early blight, posing a significant risk to food security. To identify blight diseases, agrarians visually inspect potato leaf color variations, which is time-consuming and computationally expensive. To address this challenge, various advanced technologies, such as image processing, machine learning, and deep neural networks, have been widely applied in agricultural domains, especially for autonomous disease identification and classification. However, there is an urgent need to develop computational models that can rapidly and objectively detect these diseases, even in their early stages. Therefore, this paper introduces the “PotCapsNet" method that utilizes parallel atrous convolutional layers with varying dilation rates for multi-scale feature extraction, a shuffled convolutional block attention module for effective feature selection, and a capsule network for early disease classification. The effectiveness of the proposed model was validated using the publicly available PlantVillage and PLD datasets for multi-class potato leaf disease identification. The experimental findings show that the proposed method outperforms other methods in terms of accuracy, specificity, F1 score, and sensitivity. The proposed method attained average recognition accuracy of 97.81%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document} and 97.95%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document} on the PlantVillage and PLD datasets, respectively, the best among all the considered methods. The code and datasets of the proposed method are available at https://github.com/ersachingupta11/PotCapsNet. [ABSTRACT FROM AUTHOR]

Fruits and vegetables are major sources of nutrients for the majority of the population across the globe. With the rapid increase in population, the objectives of the future agro-industry are to reduce product loss while increasing product quality and productivity considerably. Consequently, farmers need to be assisted with cutting-edge technologies for sustainable, eco-friendly, and efficient farming. Smart farming for early disease recognition and control is the current hot-spot research objective in the fruitage domain. The precision agriculture era has been revolutionized by federating cutting-edge technologies like machine learning, deep learning, and, the Internet-of-Things. However, the existing studies focused on the impact of individual technology on single or multiple cultivars of edible fruits or vegetables. Limited areas of the fruitage disease remain explored, necessitating further investigation into the research gaps and challenges identified for implementing the smart practices in real-field farmlands. In this paper, a comprehensive survey of recent advancements in fruit and vegetable disease identification and classification is presented. The technology-wise state-of-the-art findings, gaps, challenges, and future opportunities for fruitage disease recognition have been presented, covering 99 research articles. Moreover, the corpus of publicly available fruit and vegetable datasets has been investigated, with the existing gaps, improvements, and future requirements. The research paper concludes with challenges and a future outlook that promises to be a very significant and valuable resource for researchers working in the area of agronomic disease monitoring. [ABSTRACT FROM AUTHOR]

With the advancement of technologies, automatic plant leaf disease detection has received considerable attention from researchers working in the area of precision agriculture. A number of deep learning-based methods have been introduced in the literature for automated plant disease detection. However, the majority of datasets collected from real fields have blurred background information, data imbalances, less generalization, and tiny lesion features, which may lead to over-fitting of the model. Moreover, the increased parameter size of deep learning models is also a concern, especially for agricultural applications due to limited resources. In this paper, a novel ClGan (Crop Leaf Gan) with improved loss function has been developed with a reduced number of parameters as compared to the existing state-of-the-art methods. The generator and discriminator of the developed ClGan have been encompassed with an encoder–decoder network to avoid the vanishing gradient problem, training instability, and non-convergence failure while preserving complex intricacies during synthetic image generation with significant lesion differentiation. The proposed improved loss function introduces a dynamic correction factor that stabilizes learning while perpetuating effective weight optimization. In addition, a novel plant leaf classification method ClGanNet, has been introduced to classify plant diseases efficiently. The efficiency of the proposed ClGan was validated on the maize leaf dataset in terms of the number of parameters and FID score, and the results are compared against five other state-of-the-art GAN models namely, DC-GAN, W-GAN, W G a n G P , InfoGan, and LeafGan. Moreover, the performance of the proposed classifier, ClGanNet, was evaluated with seven state-of-the-art methods against eight parameters on the original, basic augmented, and ClGan augmented datasets. Experimental results of ClGanNet have outperformed all the considered methods with 99.97% training and 99.04% testing accuracy while using the least number of parameters. • A novel variant of GAN, ClGan has been introduced for augmenting maize leaf images. • A new method namely, ClGanNet has been introduced for the early identification of maize leaf disease. • Computational cost of the proposed ClGanNet has been improved by reducing the parameter size by 40%. • Finally, the performance of the proposed ClGanNet has been validated in terms of six parameters. [ABSTRACT FROM AUTHOR]