As the world becomes increasingly interconnected, emerging and innovative sensing technologies are shaping the future of agriculture, with a special focus on sustainability-related issues. In this ...context, we envision the possibility to exploit Social Internet of Things for sensing of environmental conditions (solar radiation, humidity, air temperature, and soil moisture) and communications, deep learning for plant disease detection, and crowdsourcing for images collection and classification, engaging farmers and community garden owners and experts. Through, data fusion and deep learning, the designed system can exploit the collected data and predict when a plant would (or not) get a disease, with a specific degree of precision, with the final purpose to render agriculture more sustainable. We here present the architecture, the deep learning model, and the responsive Web app. Finally, some experimental evaluations and usability/engagement tests are reported and discussed, together with final remarks, limitations, and future work.
Early identification and prevention of various plant diseases is a key feature of precision agriculture technology. This paper presents a high-performance real-time fine-grain object detection ...framework that addresses several obstacles in plant disease detection that hinders the performance of traditional methods, such as dense distribution, irregular morphology, multi-scale object classes, textural similarity. The proposed model is built on an improved version of the You Only Look Once (YOLOv4) algorithm. The modified network architecture maximizes both detection accuracy and speed by including the DenseNet in the backbone to optimize feature transfer and reuse; two new residual blocks in backbone and neck enhance feature extraction and reduce computing cost; the Spatial Pyramid Pooling (SPP) enhances receptive field, and a modified Path Aggregation Network (PANet) preserves fine-grain localized information and improves feature fusion. Additionally, use of the Hard-Swish function as the primary activation improved the model’s accuracy due to better nonlinear feature extraction. The proposed model is tested in detecting four different diseases in tomato plants under various challenging environments. The model outperforms the existing state-of-the-art detection models in detection accuracy and speed. At a detection rate of 70.19 FPS, the proposed model obtained a precision value of 90.33%, F1-score of 93.64%, and a mean average precision (mAP) value of 96.29%. Current work provides an effective and efficient method for detecting different plant diseases in complex scenarios that can be extended to different fruit and crop detection, generic disease detection, and various automated agricultural detection processes.
Plant diseases cause huge losses by changing the quality and quantity of harvested crops. Many disease symptoms caused by bacteria or fungi rely on the involvement of plant hormones, while other ...plant hormones act as defense signals in the plant. In this review the role of auxins in these processes will be evaluated. Some growth promoting plant hormones cause disease symptoms. For example auxins stimulate cell division and cell elongation in a healthy plant, but tumor formation after bacterial infection. Thus, control of auxin levels and auxin signaling pathways significantly contribute to the defense network in plants. Auxin can also act directly as defense molecule with antimicrobial activity. Since much research has been done in the recent years on auxin as a pathogenicity factor for many diseases, several examples will be presented to highlight the complexity between normal plant growth, which is regulated by auxin, and processes determining resistance or susceptibility, triggered by the same class of molecules.
Plant disease management faces ever-growing challenges due to： （i） increasing demands for total, safe and diverse foods to support the booming global population and its improving living standards; ...（ii） reducing production potential in agriculture due to competition for land in fertile areas and exhaustion of marginal arable lands; （iii） deteriorating ecology of agro-ecosystems and depletion of natural resources; and （iv） increased risk of disease epidemics resulting from agricultural intensification and monocultures. Future plant disease management should aim to strengthen food security for a stable society while simultaneously safeguarding the health of associated ecosystems and reducing dependency on natural resources. To achieve these multiple functionalities, sustainable plant disease management should place emphases on rational adaptation of resistance, avoidance, elimination and remediation strategies individually and collectively, guided by traits of specific host-pathogen associations using evolutionary ecology principles to create environmental （biotic and abiotic） conditions favorable for host growth and development while adverse to pathogen reproduction and evolution.
Plants are recognized as essential as they are the primary source of humanity's energy production since they are having nutritious, medicinal, etc. values. At any time between crop farming, plant ...diseases can affect the leaf, resulting in enormous crop production damages and economic market value. Therefore, in the farming industry, identification of leaf disease plays a crucial role. It needs, however, enormous labor, greater preparation time, and comprehensive plant pathogen knowledge. For the identification of plant disease detection various machine learning (ML) as well as deep learning (DL) methods are developed & examined by various researchers, and many of the times they also got significant results in both cases. Motivated by those existing works, here in this article we are comparing the performance of ML (Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent (SGD)) & DL (Inception-v3, VGG-16, VGG-19) in terms of citrus plant disease detection. The disease classification accuracy (CA) we received by experimentation is quite impressive as DL methods perform better than that of ML methods in case of disease detection as follows: RF-76.8% > SGD-86.5% > SVM-87% > VGG-19–87.4% > Inception-v3–89% > VGG-16–89.5%. From the result, we can tell that RF is giving the least CA whereas VGG-16 is giving the best in terms of CA.
Phytopathogenic bacteria cause severe economic losses in agricultural production worldwide. The spread rates, severity, and emerging plant bacterial diseases have become serious threat to the ...sustainability of food sources and the fruit industry. Detection and diagnosis of plant diseases are imperative in order to manage plant diseases in field conditions, greenhouses, and food storage conditions as well as to maximize agricultural productivity and sustainability. To date, various techniques including, serological, observation-based, and molecular methods have been employed for plant disease detection. These methods are sensitive and specific for genetic identification of bacteria. However, these methods are specific for genetic identification of bacteria. Currently, the innovative biosensor-based disease detection technique is an attractive and promising alternative. A biosensor system involves biological recognition and transducer active receptors based on sensors used in plant-bacteria diagnosis. This system has been broadly used for the rapid diagnosis of plant bacterial pathogens. In the present review, we have discussed the conventional methods of bacterial-disease detection, however, the present review mainly focuses on the applications of different biosensor-based techniques along with point-of-care (POC), robotics, and cell phone-based systems. In addition, we have also discussed the challenges and limitations of these techniques.
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•Early detection of a disease is essential to prevent crop loss.•The conventional techniques are sensitive and specific for pathogenic identification.•Biosensor-based techniques are innovative and promising alternatives.•Biosensor-based systems are attractive and efficient for early detection of pathogen.
Nitrogen (N) is one of the most important elements that has a central impact on plant growth and yield. N is also widely involved in plant stress responses, but its roles in host-pathogen ...interactions are complex as each affects the other. In this review, we summarize the relationship between N nutrition and plant disease and stress its importance for both host and pathogen. From the perspective of the pathogen, we describe how N can affect the pathogen's infection strategy, whether necrotrophic or biotrophic. N can influence the deployment of virulence factors such as type III secretion systems in bacterial pathogen or contribute nutrients such as gamma-aminobutyric acid to the invader. Considering the host, the association between N nutrition and plant defence is considered in terms of physical, biochemical and genetic mechanisms. Generally, N has negative effects on physical defences and the production of anti-microbial phytoalexins but positive effects on defence-related enzymes and proteins to affect local defence as well as systemic resistance. N nutrition can also influence defence via amino acid metabolism and hormone production to affect downstream defence-related gene expression via transcriptional regulation and nitric oxide (NO) production, which represents a direct link with N. Although the critical role of N nutrition in plant defences is stressed in this review, further work is urgently needed to provide a comprehensive understanding of how opposing virulence and defence mechanisms are influenced by interacting networks.
The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able ...to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.
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