Recent advances of application of optical imaging techniques for disease detection in fruits and vegetables: A review

Teet, Sudau Eh; Hashim, Norhashila

doi:10.1016/j.foodcont.2023.109849

Cited by 14 publications

(4 citation statements)

References 59 publications

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“…In greenhouse planting environments, various types of diseases affect different vegetables, and the occurrence time, types, and symptoms of the diseases can vary even within a specific type of vegetable 31 . Although several intelligent methods and tools have been developed for the detection of vegetable diseases 32 , achieving accurate detection results in greenhouse environments remains a formidable task due to the intricate nature of the diseases.…”

Section: Introductionmentioning

confidence: 99%

Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment

Wang,

Liu

2024

Sci Rep

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This study introduces YOLOv8n-vegetable, a model designed to address challenges related to imprecise detection of vegetable diseases in greenhouse plant environment using existing network models. The model incorporates several improvements and optimizations to enhance its effectiveness. Firstly, a novel C2fGhost module replaces partial C2f. with GhostConv based on Ghost lightweight convolution, reducing the model’s parameters and improving detection performance. Second, the Occlusion Perception Attention Module (OAM) is integrated into the Neck section to better preserve feature information after fusion, enhancing vegetable disease detection in greenhouse settings. To address challenges associated with detecting small-sized objects and the depletion of semantic knowledge due to varying scales, an additional layer for detecting small-sized objects is included. This layer improves the amalgamation of extensive and basic semantic knowledge, thereby enhancing overall detection accuracy. Finally, the HIoU boundary loss function is introduced, leading to improved convergence speed and regression accuracy. These improvement strategies were validated through experiments using a self-built vegetable disease detection dataset in a greenhouse environment. Multiple experimental comparisons have demonstrated the model's effectiveness, achieving the objectives of improving detection speed while maintaining accuracy and real-time detection capability. According to experimental findings, the enhanced model exhibited a 6.46% rise in mean average precision (mAP) over the original model on the self-built vegetable disease detection dataset under greenhouse conditions. Additionally, the parameter quantity and model size decreased by 0.16G and 0.21 MB, respectively. The proposed model demonstrates significant advancements over the original algorithm and exhibits strong competitiveness when compared with other advanced object detection models. The lightweight and fast detection of vegetable diseases offered by the proposed model presents promising applications in vegetable disease detection tasks.

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Section: Introductionmentioning

confidence: 99%

Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment

Wang,

Liu

2024

Sci Rep

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“…The increasing adoption of optical imaging, machine vision and artificial intelligence techniques in vegetable disease detection and management has resulted in increasing demand for these applications in various areas of precision agriculture ( Teet and Hashim, 2023 ). Convolutional Neural Network (CNN) approaches, in particular, are a promising option offered by more recent techniques utilizing modern technologies ( Gu et al., 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

Uddin,

Mazumder,

Prity

et al. 2024

Front. Plant Sci.

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Cauliflower cultivation plays a pivotal role in the Indian Subcontinent’s winter cropping landscape, contributing significantly to both agricultural output, economy and public health. However, the susceptibility of cauliflower crops to various diseases poses a threat to productivity and quality. This paper presents a novel machine vision approach employing a modified YOLOv8 model called Cauli-Det for automatic classification and localization of cauliflower diseases. The proposed system utilizes images captured through smartphones and hand-held devices, employing a finetuned pre-trained YOLOv8 architecture for disease-affected region detection and extracting spatial features for disease localization and classification. Three common cauliflower diseases, namely ‘Bacterial Soft Rot’, ‘Downey Mildew’ and ‘Black Rot’ are identified in a dataset of 656 images. Evaluation of different modification and training methods reveals the proposed custom YOLOv8 model achieves a precision, recall and mean average precision (mAP) of 93.2%, 82.6% and 91.1% on the test dataset respectively, showcasing the potential of this technology to empower cauliflower farmers with a timely and efficient tool for disease management, thereby enhancing overall agricultural productivity and sustainability

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“…With the growth of new plants, illnesses continue to be propagated and spread by rain, insects, and other means, culminating in an irreversible condition [11] and causing widespread sores and infections. Eventually, the fruit wilts, and the leaves wither, resulting in significant economic waste [12]. The most efficient technique to manage illnesses is to spray pesticides to kill hazardous germs.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Pretreatment Combined with Microwave-Assisted Hydrodistillation for Extraction of Essential Oil from Melaleuca bracteata ‘Revolution Gold’ Leaves Scales Induced by Cellulase-Inorganic Salt and Its Anti-Fungal Activity

Huang,

Zhang,

Zeng

et al. 2024

Separations

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In order to further develop the commercial use of Melaleuca bracteata (F. Muell), this report studied the extraction of essential oil from Melaleuca bracteata (F. Muell) leaves using ultrasonic pretreatment, cellulase-inorganic salt soaked and combined with microwave-assisted hydrodistillation. To optimize the primary contributing parameters, the Box–Behnken design (BBD) was applied. The optimum yield of essential oil was 9.61 mL/kg DW at a microwave power of 510.77 W, lithium chloride dose of 63.56 μmol, and microwave irradiation period of 46.97 min. The essential oil included a total of 41 compounds, and methyl eugenol (76.53%) and methyl cinnamate (12.62%) were the main compounds. The inhibitory impact was notable when the essential oil concentration was 1.6 mg/mL. Therefore, it has the potential to replace chemical pesticides. When the concentration of the essential oil solution was 1.6 mg/mL, the three pathogenic species of fungus (Pseudocercospora psidii, Colletotrichum eriobotryae, and Colletotrichum siamense) were greatly affected; at this dose, the fungus was unable to develop and its growth diameter was 0 mm. Additionally, the fungus’s inhibition rate reached 100%.

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Recent advances of application of optical imaging techniques for disease detection in fruits and vegetables: A review

Cited by 14 publications

References 59 publications

Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment

Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

Ultrasonic Pretreatment Combined with Microwave-Assisted Hydrodistillation for Extraction of Essential Oil from Melaleuca bracteata ‘Revolution Gold’ Leaves Scales Induced by Cellulase-Inorganic Salt and Its Anti-Fungal Activity

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