Design of an intelligent bean cultivation approach using computer vision, IoT and spatio-temporal deep learning structures

Devi, N. Bharatha; Sarma, Kandarpa Kumar; Laskar, Shakuntala

doi:10.1016/j.ecoinf.2023.102044

Cited by 21 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system takes images of the crop as sensor inputs, and the available learning models identify spatiotemporal characteristics of the samples, allowing realtime discrimination between healthy and diseased leaves and detection of weeds in the crop. The results demonstrate that the proposed models can accurately predict the health status of leaves and identify weeds with high precision, enabling targeted spraying of pesticides or herbicides exactly where necessary [9].…”

Section: Detection Prediction and Monitoring Of Diseases Pests And We...mentioning

confidence: 91%

“…In addition to the decision to activate the irrigation system at the appropriate timing, it is crucial to ensure water conservation. An irrigation control system utilizes sensors and IoT devices for data collection, along with machine learning (ML) and deep learning (DL) techniques for data processing and decision-making regarding the irrigation operation [9].…”

Section: Water Analysis and Optimal Irrigationmentioning

confidence: 99%

See 1 more Smart Citation

The Role of Artificial Intelligence and Machine Learning in Smart and Precision Agriculture

Bezas,

Foteini Filippidou

2023

ijcs

View full text Add to dashboard Cite

In recent years, the agricultural sector has been undergoing a new "green revolution" characterized by the increasing use of information and communication technology (ICT) and the transition from traditional farming methods to smart agricultural practices, also known as Agriculture 4.0. Robotics, combined with the use of drones, the emerging field of the Internet of Things (IoT), machine learning, and artificial intelligence, are now being deployed in digital transformation services in agriculture, aiming to optimize crop performance and agricultural sustainability. According to research and international literature, the new trends are now oriented towards the development of global and state-of-the-art connected agricultural systems through digital management platforms, with the goal of facilitating the flow of data and information. Although many efforts are being made to implement smart agriculture, there are still challenges that require further research.

show abstract

Section: Detection Prediction and Monitoring Of Diseases Pests And We...mentioning

confidence: 91%

Section: Water Analysis and Optimal Irrigationmentioning

confidence: 99%

The Role of Artificial Intelligence and Machine Learning in Smart and Precision Agriculture

Bezas,

Foteini Filippidou

2023

ijcs

View full text Add to dashboard Cite

show abstract

“…The first convolution operation, with a kernel size of 1*1, was utilized to increase the dimension of the input feature matrix. MBConv6 in Table 1 signified that the scale of convolution kernels was 6 times that of the The feature learning of the impurity-containing maize images was conducted through eight convolution stages; as shown in Table 1, the width and depth of each stage were closely related to the dimension of the original images, which were obtained by multiplying the magnification factor corresponding to the resolution with the parameters of the baseline (EfficientNetB0) [45,46] (where H i * W i * C i are the dimensions of the feature matrix before operation O i in Figure 2). L i denotes the quantity of repetitions of the operation O i , i.e., the depth of stage i.…”

Section: Image Feature Learning Networkmentioning

confidence: 99%

A Tiny Object Detection Approach for Maize Cleaning Operations

Yu,

Li,

et al. 2023

Foods

View full text Add to dashboard Cite

Real-time and accurate awareness of the grain situation proves beneficial for making targeted and dynamic adjustments to cleaning parameters and strategies, leading to efficient and effective removal of impurities with minimal losses. In this study, harvested maize was employed as the raw material, and a specialized object detection network focused on impurity-containing maize images was developed to determine the types and distribution of impurities during the cleaning operations. On the basis of the classic contribution Faster Region Convolutional Neural Network, EfficientNetB7 was introduced as the backbone of the feature learning network and a cross-stage feature integration mechanism was embedded to obtain the global features that contained multi-scale mappings. The spatial information and semantic descriptions of feature matrices from different hierarchies could be fused through continuous convolution and upsampling operations. At the same time, taking into account the geometric properties of the objects to be detected and combining the images’ resolution, the adaptive region proposal network (ARPN) was designed and utilized to generate candidate boxes with appropriate sizes for the detectors, which was beneficial to the capture and localization of tiny objects. The effectiveness of the proposed tiny object detection model and each improved component were validated through ablation experiments on the constructed RGB impurity-containing image datasets.

show abstract

“…Technology like the Internet of Things (IoT) and Unmanned Aerial Vehicles (UAV) could have a significant effect on agronomic crops and plants [7,8]. Due to the common occurrence of plant illness, manual disease detection is a critical task in the agricultural sector.…”

Section: Introductionmentioning

confidence: 99%

A novel CNN gap layer for growth prediction of palm tree plantlings

Kumar,

Rajmohan,

Adeola Ajagbe

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%.

show abstract

Design of an intelligent bean cultivation approach using computer vision, IoT and spatio-temporal deep learning structures

Cited by 21 publications

References 29 publications

The Role of Artificial Intelligence and Machine Learning in Smart and Precision Agriculture

The Role of Artificial Intelligence and Machine Learning in Smart and Precision Agriculture

A Tiny Object Detection Approach for Maize Cleaning Operations

A novel CNN gap layer for growth prediction of palm tree plantlings

Contact Info

Product

Resources

About