Advances in sensing plant diseases by imaging and machine learning methods for precision crop protection

Chadha, Sonia; Sharma, M.; Sayyed, Aaliya

doi:10.1016/b978-0-323-85193-0.00012-7

Cited by 5 publications

(5 citation statements)

References 139 publications

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“…These positive indicators on the growing impact of ML in precision crop protection are supported by numerous applications and case studies outlined in detail in quite a few recent scientific reviews ( Behmann et al., 2015 ; Liakos et al., 2018 ; Muppala and Guruviah, 2020 ; Chadha et al., 2021 ; Saleem et al., 2021 ). An in-depth analysis of some relevant publications reveals key challenges addressed by diverse image-based or sensor technology together with ML algorithms in the specific domains of crop diseases ( Table 3 ), weeds ( Table 4 ) and plagues ( Table 5 ), as discussed hereunder.…”

Section: Scientific Impact and Relevant Contributions Of ML In Precis...mentioning

confidence: 79%

“…However, the dimensionality reduction algorithms were much more widely used in precision crop protection (11%) than the other three categories. In the case of ANN algorithms, their use has increased significantly in the last five years, counting 29,956 (Figure 4A) and 759 new publications (Figure 4B) in 2022 across all disciplines and These positive indicators on the growing impact of ML in precision crop protection are supported by numerous applications and case studies outlined in detail in quite a few recent scientific reviews (Behmann et al, 2015;Liakos et al, 2018;Muppala and Guruviah, 2020;Chadha et al, 2021;Saleem et al, 2021). An indepth analysis of some relevant publications reveals key challenges addressed by diverse image-based or sensor technology together with ML algorithms in the specific domains of crop diseases (Table 3), weeds (Table 4) and plagues (Table 5), as discussed hereunder.…”

Section: Scientific Impact and Relevant Contributions Of ML In Precis...mentioning

confidence: 96%

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Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

et al. 2023

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Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.

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Section: Scientific Impact and Relevant Contributions Of ML In Precis...mentioning

confidence: 79%

Section: Scientific Impact and Relevant Contributions Of ML In Precis...mentioning

confidence: 96%

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

et al. 2023

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“…Singh and Jajoo (2021) investigate the negative effects of F on photosynthesis, specifically in maize plants (Zea mays L.) Photosystem I (PSI) and Photosystem II (PSII) serve as the principal locations of energy conversion that convert light energy into chemical energy (Chadha et al, 2021). F has a negative effect on the activity of both photosystems, which serve as internal environmental monitors.…”

Section: Induction Of Cyclic Electron Flow In Fluoride Tolerancementioning

confidence: 99%

The role of plant growth promoting rhizobacteria in strengthening plant resistance to fluoride toxicity: a review

Singh,

Yadav,

Gautam

et al. 2023

Front. Microbiol.

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A wide variety of bacteria are present in soil but in rhizospheric area, the majority of microbes helps plant in defending diseases and facilitate nutrient uptake. These microorganisms are supported by plants and they are known as plant growth-promoting rhizobacteria (PGPR). The PGPRs have the potential to replace chemical fertilizers in a way that is more advantageous for the environment. Fluoride (F) is one of the highly escalating, naturally present contaminants that can be hazardous for PGPRs because of its antibacterial capacity. The interactions of F with different bacterial species in groundwater systems are still not well understood. However, the interaction of PGPR with plants in the rhizosphere region reduces the detrimental effects of pollutants and increases plants’ ability to endure abiotic stress. Many studies reveal that PGPRs have developed F defense mechanisms, which include efflux pumps, Intracellular sequestration, enzyme modifications, enhanced DNA repair mechanism, detoxification enzymes, ion transporter/antiporters, F riboswitches, and genetic mutations. These resistance characteristics are frequently discovered by isolating PGPRs from high F-contaminated areas or by exposing cells to fluoride in laboratory conditions. Numerous studies have identified F-resistant microorganisms that possess additional F transporters and duplicates of the well-known targets of F. Plants are prone to F accumulation despite the soil’s low F content, which may negatively affect their growth and development. PGPRs can be used as efficient F bioremediators for the soil environment. Environmental biotechnology focuses on creating genetically modified rhizobacteria that can degrade F contaminants over time. The present review focuses on a thorough systemic analysis of contemporary biotechnological techniques, such as gene editing and manipulation methods, for improving plant-microbe interactions for F remediation and suggests the importance of PGPRs in improving soil health and reducing the detrimental effects of F toxicity. The most recent developments in the realm of microbial assistance in the treatment of F-contaminated environments are also highlighted.

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“…Plant disease is a significant problem in agriculture since it directly reduces the quantity and quality of plant production. These diseases could have a huge impact on the economy negatively in the global agricultural industry (Chadha et al, 2021). Plant diseases are likely to have a detrimental impact on various plant processes such as plant growth, absorption of nutrients, photosynthesis, and fruit development.…”

Section: Introductionmentioning

confidence: 99%

Evrişimli Sinir Ağının Üzüm Bitkisi Hastalık Sınıflandırması için Kullanılması

SOFUOĞLU,

BIRANT

2023

UUJFE

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Plant disease classification is the use of machine learning techniques for determining the type of disease from the input leaf images of the plants based on certain features. It is an important research area since early identification and treatment of plant disease is critical for saving crops, preventing agricultural disasters, and improving productivity in agriculture. This study proposes a new convolutional neural network model that accurately classifies the diseases on the plant leaves for the agriculture sectors. It especially works on the classification of plant diseases for grape leaves from images by designing a deep-learning architecture. A web application was also implemented to help the agricultural workers. The experiments carried out on real-world images showed that a significant improvement (8.7%) on average was achieved by the proposed model (98.53%) against the state-of-the-art models (89.84%) in terms of accuracy.

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Advances in sensing plant diseases by imaging and machine learning methods for precision crop protection

Cited by 5 publications

References 139 publications

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

The role of plant growth promoting rhizobacteria in strengthening plant resistance to fluoride toxicity: a review

Evrişimli Sinir Ağının Üzüm Bitkisi Hastalık Sınıflandırması için Kullanılması

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