Lobna Hajji-Hedfi^1,2,*, Omaima Bargougui^1,3, Abdelhak Rhouma¹, Takwa Wannassi¹, Amira Khlif^1,3, Samar Dali^1,3, Wafa Gamaoun⁴

Phyton-International Journal of Experimental Botany, Vol.94, No.8, pp. 2255-2284, 2025, DOI:10.32604/phyton.2025.066951 - 29 August 2025

Abstract Fungal plant diseases are infections caused by pathogenic fungi that affect crops, ornamental plants, and trees. Symptoms of these diseases can include leaf spots, fruit rot, root rot, and generalized growth retardation. Fungal diseases can result in decreased quality and quantity of crops, which can have a negative economic impact on farmers and producers. Moreover, these diseases can cause environmental damage. Indeed, fungal diseases can directly affect crops by reducing plant growth and yield, as well as altering their quality and nutritional value. Although effective, the use of many chemical products is often harmful to… More >

Vijaya Choudhary^1,2,3,*, Paramita Guha^1,2, Giovanni Pau⁴

Journal on Big Data, Vol.7, pp. 1-14, 2025, DOI:10.32604/jbd.2025.065122 - 12 August 2025

Abstract Crop phenotyping plays a critical role in precision agriculture by enabling the accurate assessment of plant traits, supporting improved crop management, breeding programs, and yield optimization. However, cowpea leaves present unique challenges for automated phenotyping due to their diverse shapes, complex vein structures, and variations caused by environmental conditions. This research presents a deep learning-based approach for the classification of cowpea leaf images to support crop phenotyping tasks. Given the limited availability of annotated datasets, data augmentation techniques were employed to artificially expand the original small dataset while preserving essential leaf characteristics. Various image processing More >

Adnan Amin, Wajid Zaman^*

Phyton-International Journal of Experimental Botany, Vol.94, No.7, pp. 1973-1996, 2025, DOI:10.32604/phyton.2025.067885 - 31 July 2025

Abstract Plant-pathogen interactions involve complex biological processes that operate across molecular, cellular, microbiome, and ecological levels, significantly influencing plant health and agricultural productivity. In response to pathogenic threats, plants have developed sophisticated defense mechanisms, such as pattern-triggered immunity (PTI) and effector-triggered immunity (ETI), which rely on specialized recognition systems such as pattern recognition receptors (PRRs) and nucleotide-binding leucine-rich repeat (NLR) proteins. These immune responses activate intricate signaling pathways involving mitogen-activated protein kinase cascades, calcium fluxes, reactive oxygen species production, and hormonal cross-talk among salicylic acid, jasmonic acid, and ethylene. Furthermore, structural barriers such as callose deposition… More >

Vina Eka Aristya¹, Sri Minarsih¹, Kristamtini¹, I Gusti Komang Dana Arsana¹, Samijan¹, Setyorini Widyayanti¹, Sodiq Jauhari¹, Arif Susila¹, Ni Wayan Trisnawati¹, I Ketut Mahaputra¹, I Nyoman Suyasa¹, Opik Mahendra², Supriyanta³, Gilang Wirakusuma³, Taufan Alam³, Taryono^3,*

Phyton-International Journal of Experimental Botany, Vol.94, No.7, pp. 2055-2073, 2025, DOI:10.32604/phyton.2025.065227 - 31 July 2025

Abstract Rice, as a primary commodity, needs to be increased in production while facing the sustainability challenges of limited land, water resources, and climate change. The demand for rice productivity was not enough to rely only on the fertile fields’ ability; it is necessary to consider the rainfed land potential. Cultivation in rainfed land involves biophysical pressure, low production, and limited access to superior varieties. Participatory rice breeding aimed to identify farmers’ trait preferences and develop acceptable lines. A bottom-up approach involved 203 farmers from four rainfed fields in Indonesia, i.e., Semarang-Central Java, Kulon Progo-Yogyakarta, Tabanan-Bali,… More >

Urfa Gul^#, M. Junaid Gul^#, Gyu Sang Choi, Chang-Hyeon Park^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3811-3834, 2025, DOI:10.32604/cmc.2025.063789 - 03 July 2025

Abstract Spiking Neural Network (SNN) inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection, offering enhanced performance and efficiency in contrast to Artificial Neural Networks (ANN). Unlike conventional ANNs, which process static images without fully capturing the inherent temporal dynamics, our approach represents the first implementation of SNNs tailored explicitly for agricultural disease classification, integrating an encoding method to convert static RGB plant images into temporally encoded spike trains. Additionally, while Bernoulli trials and standard deep learning architectures like Convolutional Neural Networks (CNNs) and Fully Connected Neural Networks… More >

Shivani Sood¹, Harjeet Singh^2,*, Surbhi Bhatia Khan^3,4,5,*, Ahlam Almusharraf⁶

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2751-2787, 2025, DOI:10.32604/cmc.2025.060185 - 03 July 2025

Abstract Wheat fungal infections pose a danger to the grain quality and crop productivity. Thus, prompt and precise diagnosis is essential for efficient crop management. This study used the WFD2020 image dataset, which is available to everyone, to look into how deep learning models could be used to find powdery mildew, leaf rust, and yellow rust, which are three common fungal diseases in Punjab, India. We changed a few hyperparameters to test TensorFlow-based models, such as SSD and Faster R-CNN with ResNet50, ResNet101, and ResNet152 as backbones. Faster R-CNN with ResNet50 achieved a mean average precision More >

Mukesh Dalal^1,*, Payal Mittal²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 57-91, 2025, DOI:10.32604/cmc.2025.066056 - 09 June 2025

Abstract Deep learning-based object detection has revolutionized various fields, including agriculture. This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years, highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time. The review emphasizes the integration of advanced models, such as You Only Look Once (YOLO) v9, v10, EfficientDet, Transformer-based models, and hybrid frameworks that improve the precision, accuracy, and scalability for crop monitoring and More >

Sameera V Mohd Sagheer¹, Orwel P V², P M Ameer³, Amal BaQais⁴, Shaeen Kalathil^5,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1329-1349, 2025, DOI:10.32604/cmc.2025.063961 - 09 June 2025

Abstract The precise identification of date palm tree diseases is essential for maintaining agricultural productivity and promoting sustainable farming methods. Conventional approaches rely on visual examination by experts to detect infected palm leaves, which is time intensive and susceptible to mistakes. This study proposes an automated leaf classification system that uses deep learning algorithms to identify and categorize diseases in date palm tree leaves with high precision and dependability. The system leverages pretrained convolutional neural network architectures (InceptionV3, DenseNet, and MobileNet) to extract and examine leaf characteristics for classification purposes. A publicly accessible dataset comprising multiple… More >

Rahim Khan¹, Ihsan Rabbi¹, Umar Farooq¹, Jawad Khan^2,*, Fahad Alturise^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 827-842, 2025, DOI:10.32604/cmc.2025.063303 - 09 June 2025

Abstract Leaf disease identification is one of the most promising applications of convolutional neural networks (CNNs). This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health. In this study, a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves. The researchers utilized a dataset of 3422 images, divided into four classes: healthy, fig rust, fig mosaic, and anthracnose. These diseases can significantly reduce the yield and quality of fig tree fruit. The objective of this research is to develop a… More >

Hua Zhang, Ganghua Li^*

Phyton-International Journal of Experimental Botany, Vol.94, No.5, pp. 1367-1399, 2025, DOI:10.32604/phyton.2025.064632 - 29 May 2025

Abstract Soil naturally contains various heavy metals, however, their concentrations have reached toxic levels due to excessive agrochemical use and industrial activities. Heavy metals are persistent and non-biodegradable, causing environmental disruption and posing significant health hazards. Microbial-mediated remediation is a promising strategy to prevent heavy metal leaching and mobilization, facilitating their extraction and detoxification. Nickel (Ni), being a prevalent heavy metal pollutant, requires specific attention in remediation efforts. Plants have evolved defense mechanisms to cope with environmental stresses, including heavy metal toxicity, but such stress significantly reduces crop productivity. Beneficial microorganisms play a crucial role in… More > Graphic Abstract