Evaluation of Bollworm-Tobacco Budworm Control Strategies with ICEMM

Economic comparisons of insect pest management strategies were made for the heliothine pests, bollworm, Helicoverpa zea (Boddie), and tobacco budworm, Heliothis virescens (F.); under 18 different combinations of in-season rainfall, pest densities, insecticide use, insect resistant cotton, and densities of beneficials insects. Comparisons were made using a mechanistic simulation model of insect-plant interactions. The model, referred to as the Integrated Crop Ecosystem Management Model (ICEMM), combined modified versions of the insect model, TEXCIM 5.0, and the cotton plant model, GOSSYM. Economic returns were calculated for each management strategy under each combination of in-season rainfall condition and heliothine density. In-season rainfall conditions were categorized as dry, average, optimum, and wet. Tobacco budworm densities were none, low, medium, and high. Management strategies were: no insect management when heliothines were absent; no insect management when heliothines were present; no insect management when heliothines were present with a light or heavy density of beneficial insects; one insecticide application at a low or high rate when heliothines were present; one insecticide application at a low or high rate when heliothines were present with a light or heavy density of beneficials; transgenic cotton expressing a Cry1A gene encoding an insecticidal delta-endotoxin from Bacillus thuringiensis var. kurstaki, (Bt cotton), with no pest management; Bt cotton with a light or heavy density of beneficials; and Bt cotton with one application of insecticide at a low or high rate. The greatest net returns were obtained when heliothines were absent and insect management was not employed. When heliothines were present, the management strategy with doing nothing plus heavy beneficials resulted in the highest net returns. Optimum rain fall also improved net returns compared to other in-season rainfall rates

Development of an Integration Sensor and Instrumentation System for Measuring Crop Conditions

Precision agriculture requires reliable technology to acquire accurate information on crop conditions. Based on this information, the amount of fertilizers and pesticides for the site-specific crop management can be optimized. A ground-based integrated sensor and instrumentation system was developed to measure real-time crop conditions including Normalized Difference Vegetation Index (NDVI), biomass, crop canopy structure, and crop height. Individual sensor components has been calibrated and tested under laboratory and field conditions prior to system integration. The integration system included crop height sensor, crop canopy analyzer for leaf area index, NDVI sensor, multispectral camera, and hyperspectroradiometer. The system was interfaced with a DGPS receiver to provide spatial coordinates for sensor readings. The results show that the integration sensor and instrumentation system supports multi-source information acquisition and management in the farming field

Effect of Rice Fissure on Taste Quality of Cooked Rice

According to the change of texture attribute of cooked-rice under different fissure rate of rice, the relationship between fissure rate of rice and taste value of cooked rice were studied using correlation analysis and path analysis methods. The data of correlation analysis showed that the influence of texture attribute was significant on taste. Fissure rate had an effect on taste through hardness, gumminess, chewiness, and springiness. The data of path analysis suggested that the direct effect of gumminess was significant on taste, and the other indicators of texture attribute had indirect effect through gumminess. A regression model was constructed based on the indicators viz fissure rate, texture attributes

Zinc-oxide nanoparticles ameliorated the phytotoxic hazards of cadmium toxicity in maize plants by regulating primary metabolites and antioxidants activity

Cadmium stress is a major threat to plant growth and survival worldwide. The current study aims to green synthesis, characterization, and application of zinc-oxide nanoparticles to alleviate cadmium stress in maize (Zea mays L.) plants. In this experiment, two cadmium levels (0, 0.6 mM) were applied to check the impact on plant growth attributes, chlorophyll contents, and concentration of various primary metabolites and antioxidants under exogenous treatment of zinc-oxide nanoparticles (25 and 50 mg L-1) in maize seedlings. Tissue sampling was made 21 days after the zinc-oxide nanoparticles application. Our results showed that applying cadmium significantly reduced total chlorophyll and carotenoid contents by 52.87% and 23.31% compared to non-stress. In comparison, it was increased by 53.23%, 68.49% and 9.73%, 37.53% with zinc-oxide nanoparticles 25, 50 mg L-1 application compared with cadmium stress conditions, respectively. At the same time, proline, superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase contents were enhanced in plants treated with cadmium compared to non-treated plants with no foliar application, while it was increased by 12.99 and 23.09%, 23.52 and 35.12%, 27.53 and 36.43%, 14.19 and 24.46%, 14.64 and 37.68% by applying 25 and 50 mg L-1 of zinc-oxide nanoparticles dosages, respectively. In addition, cadmium toxicity also enhanced stress indicators such as malondialdehyde, hydrogen peroxide, and non-enzymatic antioxidants in plant leaves. Overall, the exogenous application of zinc-oxide nanoparticles (25 and 50 mg L-1) significantly alleviated cadmium toxicity in maize. It provides the first evidence that zinc-oxide nanoparticles 25 ~ 50 mg L-1 can be a candidate agricultural strategy for mitigating cadmium stress in cadmium-polluted soils for safe agriculture practice

Visual question answering model for fruit tree disease decision-making based on multimodal deep learning

Visual Question Answering (VQA) about diseases is an essential feature of intelligent management in smart agriculture. Currently, research on fruit tree diseases using deep learning mainly uses single-source data information, such as visible images or spectral data, yielding classification and identification results that cannot be directly used in practical agricultural decision-making. In this study, a VQA model for fruit tree diseases based on multimodal feature fusion was designed. Fusing images and Q&A knowledge of disease management, the model obtains the decision-making answer by querying questions about fruit tree disease images to find relevant disease image regions. The main contributions of this study were as follows: (1) a multimodal bilinear factorized pooling model using Tucker decomposition was proposed to fuse the image features with question features: (2) a deep modular co-attention architecture was explored to simultaneously learn the image and question attention to obtain richer graphical features and interactivity. The experiments showed that the proposed unified model combining the bilinear model and co-attentive learning in a new network architecture obtained 86.36% accuracy in decision-making under the condition of limited data (8,450 images and 4,560k Q&A pairs of data), outperforming existing multimodal methods. The data augmentation is adopted on the training set to avoid overfitting. Ten runs of 10-fold cross-validation are used to report the unbiased performance. The proposed multimodal fusion model achieved friendly interaction and fine-grained identification and decision-making performance. Thus, the model can be widely deployed in intelligent agriculture

Multispectral imaging systems for airborne remote sensing to support agricultural production management

This study investigated three different types of multispectral imaging systems for airborne remote sensing to support management in agricultural application and production. The three systems have been used in agricultural studies. They range from low-cost to relatively high-cost, manually operated to automated, multispectral composite imaging with a single camera and integrated imaging with custom-mounting of separate cameras. Practical issues regarding use of the imaging systems were described and discussed. The low-cost system, due to band saturation, slow imaging speed and poor image quality, is more preferable to slower moving platforms that can fly close to the ground, such as unmanned autonomous helicopters, but not recommended for low or high altitude aerial remote sensing on fixed-wing aircraft. With the restriction on payload unmanned autonomous helicopters are not recommended for high-cost systems because they are typically heavy and difficult to mount. The system with intermediate cost works well for low altitude aerial remote sensing on fixed-wing aircraft with field shapefile-based global positioning triggering. This system also works well for high altitude aerial remote sensing on fixed-wing aircraft with global positioning triggering or manually operated. The custom-built system is recommended for high altitude aerial remote sensing on fixed-wing aircraft with waypoint global positioning triggering or manually operated. Keywords: airborne remote sensing, multispectral imaging, agricultural production managemen

Current status and future directions of precision aerial application for site-specific crop management in the USA

The first variable-rate aerial application system was developed about a decade ago in the USA and since then, aerial application has benefitted from these technologies. Many areas of the United States rely on readily available agricultural airplanes or helicopters for pest management, and variable-rate aerial application provides a solution for applying field inputs such as cotton growth regulators, defoliants, and insecticides. In the context of aerial application, variable-rate control can simply mean terminating spray over field areas that do not require inputs, terminating spray near pre-defined buffer areas determined by Global Positioning, or applying multiple rates to meet the variable needs of the crop. Prescription maps for aerial application are developed using remote sensing, Global Positioning, and Geographic Information System technologies. Precision agriculture technology has the potential to benefit the agricultural aviation industry by saving operators and farmers time and money

Design of Plant Protection UAV Variable Spray System Based on Neural Networks

Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized

Design of Plant Protection UAV Variable Spray System Based on Neural Networks

Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized

Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography

Background: The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI. Results: In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with R2 and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with R2 and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the R2 and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively. Conclusions: Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density