Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Chandel, Narendra Singh; Rajwade, Yogesh Anand; Dubey, Kumkum; Chandel, Abhilash K.; Subeesh, A.; Tiwari, Mukesh

doi:10.3390/plants11233344

Cited by 9 publications

(1 citation statement)

References 66 publications

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“…For instance, Xu et al [11] used the super green feature algorithm and the maximum between-class variance (OTSU) method for segmentation, observing a remarkable segmentation effect, and was able to achieve a recognition accuracy of 94.1% when the weeding robot was traveling at a speed of 1.6 km/h. Chandel et al [12] found that the combination of computer vision and thermal RGB images helped in the high-throughput mitigation and management of crop water stress. The method proposed by Khan et al [13] can be applied on a large scale to effectively map the crop types of smallholder farms at an early stage, enabling them to plan a seamless supply of food.…”

Section: Introductionmentioning

confidence: 99%

Research on Red Jujubes Recognition Based on a Convolutional Neural Network

Guo

et al. 2023

Applied Sciences

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Red jujube is one of the most important crops in China. In order to meet the needs of the scientific and technological development of the jujube industry, solve the problem of poverty, realize the backward advantage, and promote economic development, smart agriculture is essential. The main objective of this study was to conduct an online detection study of unpicked red jujubes in order to detect as many red jujubes in the picture as possible while minimizing the occurrence of overfitting and underfitting. Experiments were conducted using the Histogram of Oriented Gradients + Support Vector Machine (HOG+SVM) traditional detection method and the You Only Look Once version 5 (YOLOV5) and Faster R-CNN modern deep learning detection methods. The precision, recall, and F1 score were compared to obtain a better algorithm. The study also introduced the AlexNet model with the main objective of attempting to combine it with other traditional algorithms to maximize accuracy. Labeling was used to label the training images in YOLOV5 and Faster Regions with CNN Features (Faster R-CNN) to train the machine model so that the computer recognized these features when it saw new unlabeled data in subsequent experiments. The experimental results show that in the online recognition detection of red jujubes, the YOLOV5 and Faster R-CNN algorithms performed better than the HOG + SVM algorithm, which presents precision, recall, and F1 score values of 93.55%, 82.79%, and 87.84% respectively; although the HOG + SVM algorithm was relatively quicker to perform. The precision of detection was obviously more important than the efficiency of detection in this study, so the YOLOV5 and Faster R-CNN algorithms were better than the HOG + SVM algorithm. In the experiments, the Faster R-CNN algorithm had 100% precision, 99.65% recall, an F1 score of 99.82%, and 83% non-underfitting images for the recognized images, all of which were higher than YOLOV5′s values, with 97.17% recall, an F1 score of 98.56%, and 64.42% non-underfitting. In this study, therefore, the Faster R-CNN algorithm works best.

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Section: Introductionmentioning

confidence: 99%