Trends and Prospect of Machine Vision Technology for Stresses and Diseases Detection in Precision Agriculture

Shin, Jaemyung; Mahmud, Sultan; Rehman, Tanzeel U.; Ravichandran, P.; Heung, Brandon; Chang, Young K.

doi:10.3390/agriengineering5010003

Cited by 30 publications

(21 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…When a plant’s leaves are healthy, the plant’s immune system strengthens and becomes better able to tackle diseases that might appear in other parts of the plant. The disorders of the vegetables such as tomato, chili, potato, and cucumber plants are frequent and can have a devastating effect on the leaves ( Qi et al., 2021 ) ( Khirade and Patil, 2015 ) ( Shin et al., 2023 ) ( Wani et al., 2022 ) ( Krithika and Veni, 2017 ). These diseases are quite dangerous because they can spread swiftly and cause much damage.…”

Section: Ai-based Automated Vegetables Disease Detection Classificationmentioning

confidence: 99%

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Jafar,

Bibi,

Naqvi

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

show abstract

Section: Ai-based Automated Vegetables Disease Detection Classificationmentioning

confidence: 99%

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Jafar,

Bibi,

Naqvi

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Initially, in precision agriculture, human intervention was essential for decision-making, with motorized equipment executing agricultural tasks [85]. However, the evolution of remotesensing technology, such as satellites, drones, ground-based sensors, and crews, empowers farmers to gather high-resolution field data, enabling well-informed decisions about crop management [86]. Precision farming has the potential to improve soil health, reduce water and fertilizer usage, and enhance both crop yield and quality.…”

Section: • Smart Precision Farmingmentioning

confidence: 99%

Climatic Shifts and Agricultural Strategies: A thorough Review on Impact of Climate Change on Food Security and Crop Productivity

Praveena K.,

Malaisamy

2024

IJECC

View full text Add to dashboard Cite

This comprehensive review delves into the intricate change of climate, assessing its profound impact on global food security and crop productivity. The examination addresses key questions, exploring the consequences of climate change on agriculture, with a specific focus on the challenges encountered in vulnerable regions such as sub-Saharan Africa and South Asia. Emphasizing the urgency of adaptive strategies for sustainable global agriculture, the review navigates through the complexities of mitigating climate-induced disruptions in crop growth patterns. Furthermore, it scrutinizes the implications of climate change on biodiversity, acknowledging the interconnectedness of ecological systems. Highlighting the imperative for innovation, the review underscores the pressing need for transformative farming methods to effectively tackle the multifaceted challenges posed by climate change. The emphasis is on ensuring the resilience of global agriculture in the face of climatic shifts. In conclusion, the review provides a compelling call to action, advocating for the prompt implementation of innovative farming practices to fortify the global agricultural sector against the evolving challenges brought about by climate change, fostering sustainability and adaptability.

show abstract

“…For this purpose, precision technology in agriculture is used. Automation can improve agriculture's quality and productivity by reducing errors and early detection of diseases in crops or animals (Shin et al, 2022). IT systems used for decision support require a sufficiently large database and constant data supply to function effectively and achieve desired outcomes.…”

Section: Modern Technologies For Enhanced Agricultural Productionmentioning

confidence: 99%

Agriculture in the face of new digitization technologies

Wrzecińska,

Czerniawska-Piątkowska,

Kowalewska

et al. 2023

UBSRAS

View full text Add to dashboard Cite

Agriculture plays a vital role in food production, resource utilization, and employment but faces challenges from population growth, climate change, and food shortages. The development of information technology has significantly contributed to the industry's development, and modern technologies such as artificial intelligence, the Internet of Things, computer vision, and machine learning have revolutionized agricultural practices. The purpose of this review is to explore the adoption of digital technologies in agriculture, with a specific focus on their application in livestock breeding. Through the examination of current literature and the utilization of various research methods, this review contributes to the existing knowledge in the field. It is established that the latest information tools allow collecting, analysing data, automating tasks and supporting decision-making, which leads to increased agricultural efficiency, resource management and sustainable development. It has been proven that modern technologies play a crucial role in increasing agricultural production, improving the efficiency of livestock and crop production. These technologies include devices and sensors, data analytics and decision support systems, as well as systems for overall farm productivity assessment. Precision technologies in agriculture, thanks to automation, sensors and machine learning, allow farmers to monitor animal health, optimise feed consumption, detect diseases at early stages and increase overall productivity. IT solutions in agriculture facilitate data processing, visualisation and decision-making, leading to lower costs, greater efficiency and improved food security. The study provides practical insights for farmers and other agricultural stakeholders who can benefit from accurate information, real-time monitoring and automated processes through the integration of modern technologies, ultimately improving agricultural practices and sustainability

show abstract

Trends and Prospect of Machine Vision Technology for Stresses and Diseases Detection in Precision Agriculture

Cited by 30 publications

References 73 publications

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Climatic Shifts and Agricultural Strategies: A thorough Review on Impact of Climate Change on Food Security and Crop Productivity

Agriculture in the face of new digitization technologies

Contact Info

Product

Resources

About