Tomato detection method using domain adaptive learning for dense planting environments

This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.

Beyond Biochar-Soil, Plant and Environment: Research Progress and Future Prospects

Since industrial revolution, the atmospheric CO2 concentration has kept a continuous increase by more than 2.2 ppm yr^-1, and approaches to almost 400 ppm at present （Jouzel 2012）. China has become the largest country of greenhouse gas emission （GHG）, and confronts with great challenge to mitigate GHG.

Teaching Reform and Practice of Plant Growth Environment Course in the Context of Application Transformation Development

Through the personnel training program,revising the syllabus,optimizing teaching contents,reforming teaching methods,strengthening practical teaching links and reforming examination methods,this paper explored the teaching reform and practice of Plant Growth Environment Course,in order to improve the teaching effect,stimulate learning interests of students,and cultivate application type talents meeting social demands.

Application Potential of Quinoa as Forage

Quinoa is a crop as both food and forage.It has a tolerance to cold,drought,and salt.It is rich in vitamins,polyphenols,flavonoids,phytosterols and other substances,and has rich nutritional value and health care,which provides great potential for being as forage.In this paper,the application potential and prospects of quinoa as forage are studied.

New insights into plant nutrient signaling and adaptation to fluctuating environments

Over the past 50 years, the Green Revolution and exploitation of heterosis have allowed cereal grain yield to keep pace with world- wide population growth. Unfortunately, plant growth and crop productivity are heavily dependent on the application of synthetic fertilizers.

Ractopamine up take by alfalfa (Medicago sativa) and wheat (Triticum aestivum) from soil

Ractopamine is a beta adrenergic agonist used as a growth promoter in swine, cattle and turkeys. To test whether ractopamine has the potential to accumulate in plants grown in contaminated soil, a greenhouse study was conducted with alfalfa（Medicago sativa） and wheat（Triticum aestivum） grown in two soils having different concentrations of organic matter（1.3% and 2.1%）, amended with 0, 0.5, and 10 μg/g of ractopamine. Plant growth ranged from 2.7 to 8.8 g dry weight（dw） for alfalfa, and 8.7 to 40 g dw for wheat and was generally greater in the higher organic matter content soil. The uptake of ractopamine in plant tissues ranged from non-detectable to 897 ng/g and was strongly dependent on soil ractopamine concentration across soil and plant tissue. When adjusted to the total fortified quantities, the amount of ractopamine taken up by the plant tissue was low, 〈 0.01% for either soil.

Rhynchophorus ferrugineus attack affects a group of compounds rather than rearranging Phoenix canariensis metabolic pathways

The red palm weevil（RPW; Rhynchophorus ferrugineus） is spreading worldwide and severely harming many palm species. However, most studies on RPW focused on insect biology, and little information is available about the plant response to the attack. In the present experiment, we used metabolomics to study the alteration of the leaf metabolome of Phoenix canariensis at initial（1^（st） stage） or advanced（2^（nd） stage）attack by RPW compared with healthy（unattacked） plants.The leaf metabolome significantly varied among treatments. At the 1^（st） stage of attack, plants showed a reprogramming of carbohydrate and organic acid metabolism; in contrast, peptides and lipid metabolic pathways underwent more changes during the 2^（nd） than 1^（st） stage of attack. Enrichment metabolomics analysis indicated that RPW attack mostly affected a particular group of compounds rather than rearranging plant metabolic pathways. Some compounds selectively affected during the 1^（st） rather than 2^（nd） stage（e.g. phenylalanine; tryptophan; cellobiose;xylose; quinate; xylonite; idonate; and iso-threonate; cellobiotol and arbutine） are upstream events in the phenylpropanoid,terpenoid and alkaloid biosynthesis. These compounds could be designated as potential markers of initial RPW attack. However,further investigation is needed to determine efficient early screening methods of RPW attack based on the concentrations of these molecules.