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High-throughput phenotype monitoring systems for field crops can not only accelerate the breeding process but also provide important data support for precision agricultural monitoring. Traditional phenotype monitoring methods for field crops relying on artificial sampling and measurement have some disadvantages including low efficiency, strong subjectivity, and single characteristics. To solve these problems, the rapid monitoring, acquisition, and analysis of phenotyping information of field crops have become the focus of current research. The research explores the systematic framing of phenotype monitoring systems for field crops. Focusing on four aspects, namely phenotyping sensors, mobile platforms, control systems, and phenotyping data preprocessing algorithms, the application of the sensor technology, structural design technology of mobile carriers, intelligent control technology, and data processing algorithms to phenotype monitoring systems was assessed. The research status of multi-scale phenotype monitoring products was summarized, and the merits and demerits of various phenotype monitoring systems for field crops in application were discussed. In the meantime, development trends related to phenotype monitoring systems for field crops in aspects including sensor integration, platform optimization, standard unification, and algorithm improvement were proposed. [ABSTRACT FROM AUTHOR]

Metastatic colorectal cancer (mCRC) has a poor prognosis. Combining chemotherapy with targeted therapy constitutes a basic form of mCRC treatment. Immune checkpoint inhibitors have been recommended for microsatellite instability mCRC, while most patients harboring microsatellite stability (MSS) or proficient mismatch repair (pMMR) are less responsive to immunotherapy. Combinational targeted therapy, including poly-ADP ribose polymerase (PARP) inhibitors, has been considered a promising way to reverse immunotherapy resistance; however, there is no clear and consistent conclusions can be drawn from the current research. Here, we report the case of a 59-year-old woman diagnosed with stage IVB MSS mCRC who received three courses of capecitabine/oxaliplatin chemotherapy combined with bevacizumab as a first-line treatment, resulting in an overall evaluation of stable disease (−25.7%). However, the occurrence of adverse events of intolerable grade 3 diarrhea and vomiting forced the cessation of this therapy. A germline BRCA2 mutation was found by next-generation sequencing, and the patient further received a combination of olaparib, tislelizumab, and bevacizumab. This treatment regime resulted in a complete metabolic response and a partial response (−50.9%) after 3 months of treatment. Mild asymptomatic interstitial pneumonia and manageable hematologic toxicity were two adverse events associated with this combination therapy. This study provides new insights into the combination of PARP inhibitors and immunotherapy for MSS mCRC patients carrying germline BRCA2 mutations. [ABSTRACT FROM AUTHOR]

The field mobile platform is an important tool for high-throughput phenotype monitoring. To overcome problems in existing field-based crop phenotyping platforms, including limited application scope and low stability, a rolling adjustment method for the wheel tread was proposed. A self-propelled three-wheeled field-based crop phenotyping platform with variable wheel tread and height above ground was developed, which enabled phenotypic information of different dry crops in different development stages. A three-dimensional model of the platform was established using Pro/E; ANSYS and ADAMS were used for static and dynamic performance. Results show that when running on flat ground, the platform has a vibration acceleration lower than 0.5 m/s2. When climbing over an obstacle with a height of 100 mm, the vibration amplitude of the platform is 88.7 mm. The climbing angle is not less than 15°. Field tests imply that the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) of a canopy measured using crop growth sensors mounted on the above platform show favorable linear correlations with those measured using a handheld analytical spectral device (ASD). Their R2 values are 0.6052 and 0.6093 and root-mean-square errors (RMSEs) are 0.0487 and 0.1521, respectively. The field-based crop phenotyping platform provides a carrier for high-throughput acquisition of crop phenotypic information. [ABSTRACT FROM AUTHOR]

Grain quality involves the appearance, nutritional, and safety attributes of grains. With the improvement of people's living standards, problems pertaining to the quality of grains have received greater attention. Modern quality detection techniques feature unique advantages including rapidness, non-destructiveness, accuracy, and efficiency in detecting grain quality. This review summarizes research progress of these techniques in detection of quality indices of grains. Particularly, the review focuses on detection techniques based on physical properties including acoustic, optical, thermal, electrical, and mechanical properties, and those simulating sensory analysis such as electronic noses, electronic tongues, and electronic eyes. According to the current technological development and application, the challenges and prospects of these techniques are demonstrated. [ABSTRACT FROM AUTHOR]

• The common species and distribution pattern of weeds in wheat fields was analyzed. • The current states of research in weed perception technologies were presented. • The trend of weed detection algorithms was summarized. • The future development of weed detection in wheat fields was proposed. Weeds pose a serious threat to the safe wheat production. They are an important factor contributing to the reduction in wheat yield and quality. The current weed control methods in wheat fields have relied primarily on chemical control. The inability to determine the precise location of weeds has led to excessive usage and low utilization rate of pesticides, causing severe pollution. As agronomic operations evolve towards Agriculture 4.0, weed control technology in wheat fields is progressively becoming more precise and intelligent. Weed detection technologies and methods in wheat fields may provide the groundwork for improving the accuracy and efficiency in weeding. This study begins with a review of common weed species and distribution pattern in wheat fields and provides an in-depth analysis of current technologies and developments for weed detection. We focused on the current states of research in spectroscopy, image, imaging spectroscopy, depth information, and multi-modal information fusion for weed detection in wheat fields. We also summarized the trend of weed detection algorithms from traditional machine learning to deep learning and proposed trends of future development in wheat field weed detection. Our study has contributed to the implementation of more automatic and precise weeds management. [ABSTRACT FROM AUTHOR]