Crop Design最新文献

Since the world's population is rising continuously, more cultivable land is being utilized for their dwellings. As a result, the amount of food supply is decreasing day by day. In order to address the food shortage, a proper plan and technological breakthroughs is must. Tomato is a kind of vegetable which has the healthy ingredients and essential for our daily food list. The proposed system suggests an IoT based tomato cultivation and pest management system, with the help of deep learning methods. In the IoT implementation, camera module and moisture sensor are used to collect images of tomato plant, soil condition respectively. Based on the moisture content, the water pump will supply the water when it necessary. Besides, the real-time images of tomato leaf will be sent to the server to identify and classify natural enemies like various insect species. In the proposed system seven types of pests are identified with the help of ten deep learning models like InceptionV3, Xception, InceptionResNetV2, MobileNet, MobileNetV2, MobileNetV3Large, MobileNetV3Small, DenseNet121, DenseNet169, DenseNet201. This study has trained with leaves and insects separately to identify whether an image from a tomato plant is insectoid or not. 458 images of pests and 912 images of leaves are utilized in the proposed architecture. The accuracy of classifying insects or leaves using DenseNet201 is 100 ​%. The highest accuracy of 94 ​% is obtained to classify the different insects using the DenseNet201 model.

The Multitrait Genotype Ideotype Distance Index (MGIDI) is a strong and adaptable technique for choosing superior genotypes of diverse crops based on numerous attributes. It is a multivariate selection indicator that incorporates different characteristic information into a single value and ranks genotypes based on their distance from an ideal genotype. Breeders can use variable selection criteria including weighting traits and assessing genetic strengths and weaknesses. It organizes attributes into components and chooses optimal genotypes based on many traits using principal component analysis. This review covered the available information regarding the background, applications, prospects, and limitations of MGIDI for crop improvement and breeding in this research. We discussed the significant discoveries and consequences of several studies that used MGIDI to enhance the productivity, excellence, and flexibility of numerous crops, such as bush yam, barley, cassava, cucumber, guar, lentil, maize, rice, bean, soybean, wheat, etc. Additionally, we talked about some of the potential applications of MGIDI for breeding and crop improvement, such as tolerance to salinity, stability analysis, tolerance to waterlogging, mechanism of drought response, performance in agronomy and tuber quality, nutritional value and productivity, adaptability, increased yield, early maturity, and stress resistance etc. Following the upward trend, MGIDI can be considered as a valuable index technique for selection of crop genotypes that can address food security, climate change, and nutritional quality problems worldwide. We expect that this study will spark more research and use of MGIDI in different crops characteristics, contributing to the improvement of plant breeding science.

Plants undergo deteriorating stress situations, which has an adverse effect on their overall growth, maturation, and development. To mitigate these situations plants undergo regulatory cellular mechanisms including epigenetic changes at both genomic as well as protein levels. Post-transcriptional as well as translational modifications of proteins enhance its dynamics and complexity along with orchestrating several cellular functions in response to external stimuli. One of the most crucial roles of Post Translational Modification is under the stress tolerance mechanisms in plants. PTM creates a fine-tuning between all regulatory networks and serves as a highly responsible phenomenon. Illustrative analysis of post-translational modification in various signaling pathways has generated new insight for designing crop cultivars towards better development with higher yield and increased tolerance. In this review, we have first introduced post-translational modification and their types. Later, we discussed the prevalent biotic-abiotic stress, plants adaptation to the stress response mechanism, and the participation of PTMs in these stress conditions to highlight better agricultural productivity.

Sugar beet, a sugar crop, faces a persistent threat from foliar and root diseases, leading to substantial yield losses. Traditional methods of disease identification and severity assessment are often time-consuming, error-prone, and impractical, particularly in large production areas. In response to this challenge, researchers have recently turned to innovative solutions involving image processing and machine learning techniques for efficient disease detection in sugar beet plants. Image processing technology has emerged as a rapid and precise disease identification technology in sugar beet. By capitalizing on the ability of image processing to differentiate coloured objects, this approach facilitates the accurate determination of disease severity, enabling timely intervention measures. The urgency of developing faster and more practical methods becomes evident, highlighting the need to decrease human errors in identifying plant diseases and assessing their severity and progression. This review showcases the potential of image processing technology in revolutionizing disease detection strategies for sugar beet crops. The ability to swiftly and accurately determine disease outbreak, severity, and progression addresses a critical gap in current agricultural practices. Image processing technology holds promise as a practical and efficient solution for large-scale disease management in sugar beet cultivation, paving the way for sustainable and high-yield sugar production.

In agricultural scenario, chitosan, a naturally occurring biopolymer derived from chitins, has the ability to function as both a bio-stimulant and an elicitor. Chitosan is produced from chitins. It is suitable for a wide range of uses because it is non-toxic, does not contaminate the environment, and is biocompatible with living things. It improves physiological reactions and lessens the negative effects of abiotic stimuli through the stress transduction pathway and the use of secondary messengers. Through nitric oxide and hydrogen peroxide-based signalling pathways, chitosan treatment activates antioxidant enzymes. Additionally, it stimulates the production of organic acids, carbohydrates, amino acids, and other metabolites needed for osmotic regulation, stress signalling, and other processes. Additionally, it can combine with heavy metals to produce compounds, and it is used in both phytoremediation and biological remediation of polluted soil. Additionally, this is applied topically to a variety of plants as an anti-transpirant agent, which reduces the quantity of water needed while also providing protection from other negative effects. Due of chitosan's exceptional properties and the way the climate is changing, sustainable farming practises are increasingly incorporating it. Our study is a compendium of current chitosan research that emphasises abiotic stress reactions. These responses could be helpful in upcoming initiatives to increase crop productivity.

The detection of weed species in rangeland environments is a challenging task due to various factors such as dense, variable species vegetation, ocular occlusion, and a wide variety of plant morphology. Most research in weed detection, however, focuses on croplands. This research addresses the need for accurate rangeland weed detection models by leveraging convolutional neural network (CNN) models enhanced with transfer learning applied to the DeepWeeds data set taken in situ in regional North Eastern Australia. It investigates the effectiveness of transfer learning across seven popular models, utilizing data augmentation and fine-tuning. The performance of these models was evaluated using accuracy metrics and compared against each other. The results demonstrated that transfer learning, coupled with fine tuning, could be a viable solution for generating efficient weed plant detection models with lower demands on computational resources and smaller datasets, despite the challenging conditions of rangeland environments. EfficientNetV2B1 had the highest classification accuracy of 94.2 ​%, and lowest training times. Moreover, high levels of accuracy were also achieved using InceptionV3, VGG16, and Densenet121, albeit with a training time penalty. This research provides insights into the performance of CNN models in challenging rangeland environments, demonstrates the potential of using transfer learning to enhance weed detection models, and underscores the significance of model selection in agricultural applications of CNNs.

Pearl millet is a key food security grain crop in the world's drylands due to its tolerance to abiotic stresses. However, its yield remains low and is negatively impacted by climate change. Root phenes are potential targets to improve crop productivity and resilience to environmental stress. However, the sheer number of combinations resulting from interactions of multiple phenes is a challenge for empirical research. In silico approaches are a plausible alternative to assess the utility of different phene combinations in varying states over diverse environmental contexts. Here, we developed an implementation of the functional-structural plant/soil model – OpenSimRoot, for pearl millet in typical sub-Sahelian soil and environmental conditions. Root architectural, anatomical, and physiological parameters were measured using a popular pearl millet variety (Souna 3) and implemented in the model. The above-ground biomass and root length density predicted by the model were similar to data from field trials. The utility of different root phenes was then evaluated for improved phosphorus uptake and plant growth in P deficient soils. Doubled root hair length and density, shallower root angle (−15°) and doubled long lateral root density were found to improve plant growth by 76 ​%, 33 ​% and 33 ​% respectively under low P conditions. Moreover, these phenes showed synergism when combined in silico and led to optimal biomass production in low P supply conditions that resulted in a 75 ​% loss of biomass in the reference variety. Our study suggests that these phenotypes could be targeted to improve biomass production in pearl millet and consequently its yield in low-P availability conditions.