Sustainable Rice Production Under Biotic and Abiotic Stress Challenges

Iqbal, Junaid; Zia-ul-Qamar,; Yousaf, Usama; Asgher, Aqsa; Dilshad, Rabia; Qamar, Fathia Mobeen; Bibi, Sajida; Rehman, Sajid Ur; Haroon, Muhammad

doi:10.1007/978-3-031-15568-0_11

Cited by 4 publications

(2 citation statements)

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“…Rice farming is under continuous exposure to a broad category of biotic (pathogen invasion and insect infestations) and abiotic (extreme temperatures, drought, cold, heavy metal toxicity, and salinity) stress-related factors leading to serious agricultural issues like poor grain production and quality deterioration [9]. Figure 1 depicts different abiotic stress-related factors that negatively impact rice farming considerably.…”

Section: Introductionmentioning

confidence: 99%

Abiotic Stress in Rice: Visiting the Physiological Response and Its Tolerance Mechanisms

Sarma,

Kashtoh,

Lama Tamang

et al. 2023

Plants

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Rice (Oryza sativa L.) is one of the most significant staple foods worldwide. Carbohydrates, proteins, vitamins, and minerals are just a few of the many nutrients found in domesticated rice. Ensuring high and constant rice production is vital to facilitating human food supplies, as over three billion people around the globe rely on rice as their primary source of dietary intake. However, the world’s rice production and grain quality have drastically declined in recent years due to the challenges posed by global climate change and abiotic stress-related aspects, especially drought, heat, cold, salt, submergence, and heavy metal toxicity. Rice’s reduced photosynthetic efficiency results from insufficient stomatal conductance and natural damage to thylakoids and chloroplasts brought on by abiotic stressor-induced chlorosis and leaf wilting. Abiotic stress in rice farming can also cause complications with redox homeostasis, membrane peroxidation, lower seed germination, a drop in fresh and dry weight, necrosis, and tissue damage. Frequent stomatal movements, leaf rolling, generation of reactive oxygen radicals (RORs), antioxidant enzymes, induction of stress-responsive enzymes and protein-repair mechanisms, production of osmolytes, development of ion transporters, detoxifications, etc., are recorded as potent morphological, biochemical and physiological responses of rice plants under adverse abiotic stress. To develop cultivars that can withstand multiple abiotic challenges, it is necessary to understand the molecular and physiological mechanisms that contribute to the deterioration of rice quality under multiple abiotic stresses. The present review highlights the strategic defense mechanisms rice plants adopt to combat abiotic stressors that substantially affect the fundamental morphological, biochemical, and physiological mechanisms.

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Section: Introductionmentioning

confidence: 99%

Abiotic Stress in Rice: Visiting the Physiological Response and Its Tolerance Mechanisms

Sarma,

Kashtoh,

Lama Tamang

et al. 2023

Plants

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“…Rice is one of the world's major food crops, and 1/3 of the world's population consumes rice as a staple food [1]. The increasing world population places higher demands on rice yield and quality [2], and rice production should increase by 50% by 2025 to accommodate the increase in world population [3].…”

Section: Introductionmentioning

confidence: 99%

A Proposal for Lodging Judgment of Rice Based on Binocular Camera

Yang,

Liang,

et al. 2023

Agronomy

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Rice lodging is a crucial problem in rice production. Lodging during growing and harvesting periods can decrease rice yields. Practical lodging judgment for rice can provide effective reference information for yield prediction and harvesting. This article proposes a binocular camera-based lodging judgment method for rice in real-time. As a first step, the binocular camera and Inertial Measurement Unit (IMU) were calibrated. Secondly, Census and Grayscale Level cost features are constructed for stereo matching of left and right images. The Cross-Matching Cost Aggregation method is improved to compute the aggregation space in the LAB color space. Then, the Winner-Takes-All algorithm is applied to determine the optimal disparity for each pixel. A disparity map is constructed, and Multi-Step Disparity Refinement is applied to the disparity map to generate the final one. Finally, coordinate transformation obtains 3D world coordinates corresponding to pixels. IMU calculates the real-time pose of the binocular camera. A pose transformation is applied to the 3D world coordinates of the rice to obtain its 3D world coordinates in the horizontal state of the camera (pitch and roll angles are equal to 0). Based on the distance between the rice and the camera level, thresholding was used to determine whether the region to be detected belonged to lodging rice. The disparity map effect of the proposed matching algorithm was tested on the Middlebury Benchmark v3 dataset. The results show that the proposed algorithm is superior to the widely used Semi-Global Block Matching (SGBM) stereo-matching algorithm. Field images of rice were analyzed for lodging judgments. After the threshold judgment, the lodging region results were accurate and could be used to judge rice lodging. By combining the algorithms with binocular cameras, the research results can provide practical technical support for yield estimation and intelligent control of rice harvesters.

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