Detecting strawberry diseases and pest infections in the very early stage with an ensemble deep-learning model

Vegetable cultivation stands as a pivotal element in the agricultural transformation illustrating a complex interplay between technological advancements, evolving environmental perspectives, and the growing global demand for food. This comprehensive review delves into the broad spectrum of developments in modern vegetable cultivation practices. Rooted in historical traditions, our exploration commences with conventional cultivation methods and traces the progression toward contemporary practices emphasizing the critical shifts that have refined techniques and outcomes. A significant focus is placed on the evolution of seed selection and quality assessment methods underlining the growing importance of seed treatments in enhancing both germination and plant growth. Transitioning from seeds to the soil, we investigate the transformative journey from traditional soil-based cultivation to the adoption of soilless cultures and the utilization of sustainable substrates like biochar and coir. The review also examines modern environmental controls highlighting the use of advanced greenhouse technologies and artificial intelligence in optimizing plant growth conditions. We underscore the increasing sophistication in water management strategies from advanced irrigation systems to intelligent moisture sensing. Additionally, this paper discusses the intricate aspects of precision fertilization, integrated pest management, and the expanding influence of plant growth regulators in vegetable cultivation. A special segment is dedicated to technological innovations, such as the integration of drones, robots, and state-of-the-art digital monitoring systems, in the cultivation process. While acknowledging these advancements, the review also realistically addresses the challenges and economic considerations involved in adopting cutting-edge technologies. In summary, this review not only provides a comprehensive guide to the current state of vegetable cultivation but also serves as a forward-looking reference emphasizing the critical role of continuous research and the anticipation of future developments in this field.

Section: Technological Innovations In Vegetable Cultivationmentioning

confidence: 99%

Advancing horizons in vegetable cultivation: a journey from ageold practices to high-tech greenhouse cultivation—a review

Ahmed,

Zhang,

Deng

et al. 2024

“…Providing data-driven decision support based on the plant growth information is the other critical capability of the MRP. There exist some common decision-making pipelines in both academia and industry, including ripeness detection ( Talha et al., 2021 ), diseases and pest identification ( Lee et al., 2022 ), and fruit counting ( Kirk et al., 2021 ). Image data captured by various perception systems have been widely used to achieve the above purpose ( Gongal et al., 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Mobile robotics platform for strawberry temporal–spatial yield monitoring within precision indoor farming systems

Ren

Bao

et al. 2023

Plant phenotyping and production management are emerging fields to facilitate Genetics, Environment, & Management (GEM) research and provide production guidance. Precision indoor farming systems (PIFS), vertical farms with artificial light (aka plant factories) in particular, have long been suitable production scenes due to the advantages of efficient land utilization and year-round cultivation. In this study, a mobile robotics platform (MRP) within a commercial plant factory has been developed to dynamically understand plant growth and provide data support for growth model construction and production management by periodical monitoring of individual strawberry plants and fruit. Yield monitoring, where yield = the total number of ripe strawberry fruit detected, is a critical task to provide information on plant phenotyping. The MRP consists of an autonomous mobile robot (AMR) and a multilayer perception robot (MPR), i.e., MRP = the MPR installed on top of the AMR. The AMR is capable of traveling along the aisles between plant growing rows. The MPR consists of a data acquisition module that can be raised to the height of any plant growing tier of each row by a lifting module. Adding AprilTag observations (captured by a monocular camera) into the inertial navigation system to form an ATI navigation system has enhanced the MRP navigation within the repetitive and narrow physical structure of a plant factory to capture and correlate the growth and position information of each individual strawberry plant. The MRP performed robustly at various traveling speeds with a positioning accuracy of 13.0 mm. The temporal–spatial yield monitoring within a whole plant factory can be achieved to guide farmers to harvest strawberries on schedule through the MRP’s periodical inspection. The yield monitoring performance was found to have an error rate of 6.26% when the plants were inspected at a constant MRP traveling speed of 0.2 m/s. The MRP’s functions are expected to be transferable and expandable to other crop production monitoring and cultural tasks.

“…Deep learning algorithms possess the ability to autonomously learn and represent features, and they can partially replace manual disease detection with their high robustness and accuracy ( Liu and Wang, 2021 ; Shao et al., 2022 ). It has been extensively utilized in the detection of diseases in maize ( Khan et al., 2023 ), potatoes ( Dai et al., 2022 ), strawberries ( Lee et al., 2022 ), citrus ( Qiu et al., 2022 ), and other crops ( Dai and Fan, 2022 ). In recent years, researchers have employed deep learning techniques to detect rice bacterial blight.…”

Section: Introductionmentioning

confidence: 99%

Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles

Pan,

Guo,

Zheng

et al. 2023

Wild rice, a natural gene pool for rice germplasm innovation and variety improvement, holds immense value in rice breeding due to its disease-resistance genes. Traditional disease resistance identification in wild rice heavily relies on labor-intensive and subjective manual methods, posing significant challenges for large-scale identification. The fusion of unmanned aerial vehicles (UAVs) and deep learning is emerging as a novel trend in intelligent disease resistance identification. Detecting diseases in field conditions is critical in intelligent disease resistance identification. In pursuit of detecting bacterial blight in wild rice within natural field conditions, this study presents the Xoo-YOLO model, a modification of the YOLOv8 model tailored for this purpose. The Xoo-YOLO model incorporates the Large Selective Kernel Network (LSKNet) into its backbone network, allowing for more effective disease detection from the perspective of UAVs. This is achieved by dynamically adjusting its large spatial receptive field. Concurrently, the neck network receives enhancements by integrating the GSConv hybrid convolution module. This addition serves to reduce both the amount of calculation and parameters. To tackle the issue of disease appearing elongated and rotated when viewed from a UAV perspective, we incorporated a rotational angle (theta dimension) into the head layer's output. This enhancement enables precise detection of bacterial blight in any direction in wild rice. The experimental results highlight the effectiveness of our proposed Xoo-YOLO model, boasting a remarkable mean average precision (mAP) of 94.95%. This outperforms other models, underscoring its superiority. Our model strikes a harmonious balance between accuracy and speed in disease detection. It is a technical cornerstone, facilitating the intelligent identification of disease resistance in wild rice on a large scale.