Crop Leaf Research Articles

Tomato Leaf Disease Detection using Flask Frame Work

In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, tomato is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, tomato are a great source of protein that offer many health benefits, but there are a lot of diseases associated with tomato leaf which hinder its production. Thus, an accurate classification of tomato leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify leaf disease by using public dataset of leaf image and CNN model with the open source library TensorFlow. In this project, we proposed a method to classify tomato leaf disease and to find and describe the efficient network architecture (hyper parameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying tomato leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using CNN architecture check if we could get faster training times, higher accuracy and easier retraining. Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this project, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this project will be a valuable resource for researchers who study the detection of plant diseases. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

Estimating and partitioning evapotranspiration in a film mulched cropland with shallow groundwater by the improved dual source model

Evapotranspiration (ET) is a key link in terrestrial water cycle and influenced by complex environmental factors in cropland. Accurate assessment of crop ET over heterogeneous underlying surfaces remains challenging. In this study, the dual source Shuttleworth-Wallace (SW) model was modified to estimate λET (latent heat corresponding to ET) through incorporating the effects of film mulching and shallow groundwater. λET and latent heat from transpiration (λT) were measured in a maize field in northwest China during 2017 and 2018 to assess the performance of the original (SW) and modified (SWM) models. Results indicate that the overestimation of latent heat from soil evaporation (λE) due to ignoring film mulching could be obviously mitigated by SWM. Besides, considering capillary rise from shallow groundwater in SWM improved not only λET estimates but also the accuracy of canopy resistance parameter. The SWM model outperformed the SW model that the slopes of fitting lines for λET and λT increased from 0.60 to 0.79 to 0.77–0.99 at daily scale and Nash-Sutcliffe Efficiency coefficient exceeded 0.86. Relative error and RMSE were decreased to below 0.15 and 19.88 W m−2, respectively. The estimations of λET and λT by SWM were also improved at hourly scale. Seasonal variation and partition of λET between two years were comparable and average T/ET for the entire growing season was about 78 %-84 %, which was largest at the heading stage. Path analysis on the responses of λET to the environmental factors recognized the greatest direct effect of crop height or leaf area index, and identified the significantly direct control of shallow groundwater on λT in this area. The study presented an improved dual source model to better quantify λET, which would benefit water resources management for film mulched croplands with shallow groundwater.

Proposals for new spray drift exposure values in orchards and vineyards for residents and bystanders

In the EU, predicted exposure to spray drift for residents and bystanders from applications in orchards and vineyards is based on data from one study published in 1987, where one downwind distance (8 m) was considered. CropLife Europe conducted sixteen new GLP compliant studies in 4 EU countries, 8 in orchards, 8 in vineyards with early and late season applications, using adult and child mannequins located 5, 10 and 15 m downwind from the last row to measure dermal and inhalation exposures. The resulting “Bystander Resident Orchard Vineyard (BROV)” database comprises 288 observations and offers a more comprehensive option for exposure prediction.There were differences between adult and child, crop type, leaf cover and distance from the sprayer, supporting the derivation of mean, median, 75th and 95th percentile exposures for each subset. Exposures did not generally correlate with wind speed, wind direction, sprayer type, spray quality, spray concentration or amount applied. Dermal and inhalation exposure were lower in vineyards than in orchards and further analysis is required to understand why.

Classification and Identification of Pinecone Mulching in Blueberry Cultivation Based on Crop Leaf Characteristics and Hyperspectral Data

While crushed pinecone mulch holds promise as a beneficial material for blueberry cultivation, research on its effectiveness remains limited. Crop leaf characteristics can serve as parameters for assessing mulching effects, although there are several limitations, including the need to analyze various distinct characteristics separately. The combination of hyperspectral data and machine learning techniques is expected to enable the selection of only the most important features among these characteristics. In this study, we investigated the impact of various mulching treatments utilizing pine tree byproducts, including crushed pinecones. Mulching variations included non-mulching (NM), crushed pinecones (PCs), a mixture of crushed pinecones and sulfur (PCS), pine needles (PNs), and sulfur treatment (S). Conventional methods were employed to measure leaf growth (length and width) and physiological characteristics (chlorophyll content, chlorophyll fluorescence, and stomatal conductance). Hyperspectral reflectance was also measured, and classification models using Partial Least Squares Discriminant Analysis (PLS-DA) and eXtreme Gradient Boosting (XGBoost) were developed for crop characteristics, vegetation indices (VIs), visible and near-infrared (VNIR), and short-wave infrared (SWIR). The results showed that using crushed pinecones as the sole mulching material for blueberries, without sulfur treatment, had a positive impact on blueberry growth. The PC treatment exhibited a dual effect on plant growth by lowering the soil pH to 5.89 and maintaining soil moisture within the range of 26.33–35.20%. We observed distinct differences in soil inorganic nutrient content, with higher concentrations of organic matter, total nitrogen, and available P2O5 and K+, which positively influenced blueberry growth. Mulching treatments demonstrated superior physiological characteristics, with two classification models identifying stomatal conductance (gs) as a key parameter influencing treatment classification (VIP scores > 1 rank: 3, variable score rank: 1). The photochemical reflectance index (PRI) emerged as a major parameter among VIs, showing potential for measuring water stress (VIP scores > 1 rank: 2, variable score rank: 1). In the SWIR PLS-DA model, wavelength peaks were mainly observed in the O-H overtone (1410 nm, 1450 nm, 1930 nm, 1940 nm, and 2100 nm). Overall, crushed pinecones were found to positively impact the initial growth of blueberries by enhancing water status (plant respiration).

Crop Leaf Phenotypic Parameter Measurement Based on the RKM-D Point Cloud Method.

Crop leaf length, perimeter, and area serve as vital phenotypic indicators of crop growth status, the measurement of which is important for crop monitoring and yield estimation. However, processing a leaf point cloud is often challenging due to cluttered, fluctuating, and uncertain points, which culminate in inaccurate measurements of leaf phenotypic parameters. To tackle this issue, the RKM-D point cloud method for measuring leaf phenotypic parameters is proposed, which is based on the fusion of improved Random Sample Consensus with a ground point removal (R) algorithm, the K-means clustering (K) algorithm, the Moving Least Squares (M) method, and the Euclidean distance (D) algorithm. Pepper leaves were obtained from three growth periods on the 14th, 28th, and 42nd days as experimental subjects, and a stereo camera was employed to capture point clouds. The experimental results reveal that the RKM-D point cloud method delivers high precision in measuring leaf phenotypic parameters. (i) For leaf length, the coefficient of determination (R2) surpasses 0.81, the mean absolute error (MAE) is less than 3.50 mm, the mean relative error (MRE) is less than 5.93%, and the root mean square error (RMSE) is less than 3.73 mm. (ii) For leaf perimeter, the R2 surpasses 0.82, the MAE is less than 7.30 mm, the MRE is less than 4.50%, and the RMSE is less than 8.37 mm. (iii) For leaf area, the R2 surpasses 0.97, the MAE is less than 64.66 mm2, the MRE is less than 4.96%, and the RMSE is less than 73.06 mm2. The results show that the proposed RKM-D point cloud method offers a robust solution for the precise measurement of crop leaf phenotypic parameters.

Identification of leaf diseases in field crops based on improved ShuffleNetV2.

Rapid and accurate identification and timely protection of crop disease is of great importance for ensuring crop yields. Aiming at the problems of large model parameters of existing crop disease recognition methods and low recognition accuracy in the complex background of the field, we propose a lightweight crop leaf disease recognition model based on improved ShuffleNetV2. First, the repetition number and the number of output channels of the basic module of the ShuffleNetV2 model are redesigned to reduce the model parameters to make the model more lightweight while ensuring the accuracy of the model. Second, the residual structure is introduced in the basic feature extraction module to solve the gradient vanishing problem and enable the model to learn more complex feature representations. Then, parallel paths were added to the mechanism of the efficient channel attention (ECA) module, and the weights of different paths were adaptively updated by learnable parameters, and then the efficient dual channel attention (EDCA) module was proposed, which was embedded into the ShuffleNetV2 to improve the cross-channel interaction capability of the model. Finally, a multi-scale shallow feature extraction module and a multi-scale deep feature extraction module were introduced to improve the model's ability to extract lesions at different scales. Based on the above improvements, a lightweight crop leaf disease recognition model REM-ShuffleNetV2 was proposed. Experiments results show that the accuracy and F1 score of the REM-ShuffleNetV2 model on the self-constructed field crop leaf disease dataset are 96.72% and 96.62%, which are 3.88% and 4.37% higher than that of the ShuffleNetV2 model; and the number of model parameters is 4.40M, which is 9.65% less than that of the original model. Compared with classic networks such as DenseNet121, EfficientNet, and MobileNetV3, the REM-ShuffleNetV2 model not only has higher recognition accuracy but also has fewer model parameters. The REM-ShuffleNetV2 model proposed in this study can achieve accurate identification of crop leaf disease in complex field backgrounds, and the model is small, which is convenient to deploy to the mobile end, and provides a reference for intelligent diagnosis of crop leaf disease.

Strong Agricultural Resilience to 2022 Southern China Drought

AbstractMeteorological drought, especially when influenced by human activities, significantly impacts agriculture. We assessed the Yangtze River Basin (YRB)’s crop ecosystem resilience during the 2022 southern China drought. Using the elasticity framework, we quantified crop Leaf Area Index (LAI) changes with meteorological factors and human activities (irrigation) in the YRB. Our study covered maize, wheat, early and late‐season rice, evaluating model accuracy with and without irrigation. Results indicate high accuracy (R2 > 0.8, p < 0.05) in simulating LAI changes, notably improved with irrigation. Irrigation emerged as the primary driver of LAI changes in the past two decades, except for solar radiation’s impact on maize. In 2022, irrigation crucially influenced LAI changes, particularly in rice varieties. Early‐season rice and late‐season rice saw over 40% contribution from irrigation, while maize and wheat responded mainly to precipitation and radiation. Overall, 2022 yields remained stable, with late‐season rice showing an apparent increase. Drought timing correlated with maize, wheat, and early‐season rice yields, consistent with natural patterns. Conversely, late‐season rice exhibited the opposite pattern due to artificial irrigation, impacting 58% of the growing areas in 2022. The study offers insights into investigating farmland ecosystems responding to extreme droughts.

Automated Leaf Disease Detection System with Machine Learning

Abstract: This study provides a detailed review of the application of deep learning techniques in plant protection, with a particular emphasis on the detection of crop leaf diseases. Deep learning has received a lot of attention for its success in feature extraction and machine learning, and it has emerged as a major technique in a variety of disciplines such as image and video processing, audio processing, and natural language processing. When applied to the field of plant disease detection, deep learning allows for more objective and efficient extraction of disease traits, boosting research efficiency and technical improvements.Our study seeks to present a synthesis of recent advances in deep learning applied to agricultural leaf disease detection, highlighting current trends and addressing issues in the field. The paper is an invaluable resource for scholars working on plant pest identification. Specifically, our approach uses the Convolutional Neural Network (CNN) algorithm, attaining an outstanding accuracy rate of 97% in disease identification.

Crop Disease Detection and Recommendation of Pesticides and Secondary Crops using Deep Learning

Abstract: In India, so many people belong to an agricultural background. Nowadays crop disease detection is a very crucial topic in analysis. Crop diseases are one of the major issues for the depletion of the quality and quantity of the crop. So, detecting and giving suitable solutions for crop disease is very important to increase the quality and quantity of the crops. In this research work, we use Deep Learning Algorithms such as Convolutional Neural Networks to train the crop disease dataset and we use image pre-processing techniques to pre-process the image and to detect the crop disease. In this project, we selected the convolution neural network to get better accuracy for the system. Here, crop leaf images that are affected by diseases are given as input to the system, and after pre-processing that image, it will detect whether there is a disease or not for the crop. If a disease is detected then it will give the name of the disease. After, it will show the suitable pesticides and chemicals that reduce the crop diseases for good yield of the crop. In this project, we also suggest to the farmers some alternative crops that provide various benefits such as improve yield, soil fertility, pest management, etc. These alternative crops often have unique characteristics that complement the yield and growth of main crops

INCREMENTAL LEARNING APPROACH FOR TOMATO LEAF DISEASE DETECTION WITHOUT CATASTROPHIC FORGETTING PROBLEM

In the agricultural sector, the early detection on the crop disease is one of the major factor to prevent the diseases spread out and counteract the loss. However, in some cases the disease are still detected manually by the expert which is considered time-consuming, cost a lot of money, and somewhat inconsistencies occur. In the last decades, the utilization of machine learning has proven to allow the automation of identifying diseases on the leaf plant quickly and accurately. Nonetheless, the major problem has been faced as the lack of model to recognize the crop leaf diseases on the real condition. Therefore, huge number of various kind of leaf disease sample data is necessary to feed on the model. Incremental leaning is one of the best applicable approach to keep the model up to date by continually learning the new incoming plant leaf dataset. This study aims to classify the disease on the tomato leaves using CNN (Convolutional Neural Network) current state-of-the-art and implement the incremental learning as well as reducing the catastrophic problem by Freezing the last Layer and Rehearsal proposed method. The result shows that the best performance achieved when applying the Dense-Net with 95% accuracy and the proposed method succeed to outperform the highest previous performance on incremental learning which remain on the 94% of accuracy value after conducting incremental process on the base model

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5–P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625–0.9875; user’s accuracy: 0.9583–0.9933; and producer’s accuracy: 0.9634–1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5–P7 (OA: 0.9125–0.9750; UA: 0.878–0.9778; and PA: 0.9362–0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.

In Situ Sucrose Injection for Alteration of Carbohydrate Reserve Dynamics in Grapevine

Inconsistent yield of subtropical table grape across seasons is often associated with low carbohydrate reserves during flowering. In an attempt to increase TNC and thus yield, sucrose was injected into treated trunks during periods of high carbohydrate demand (i.e., between budburst and flowering). Total non-structural carbohydrate (TNC) concentration dynamics were assessed in the grapevine root and trunk tissues of both control and treated vines. In the control (untreated) vines, the TNC concentration in root and trunk tissues was 13.5% and 7.5% w/dw at leaf fall and 7.2% and 3.7% w/dw at flowering, respectively. This decrease in carbohydrate reserve was estimated at ~500 g/vine and is associated with the re-establishment of the plant canopy in early spring. Carbohydrate reserves remained stable or rose slightly between flowering and harvest and recovered between crop harvest and leaf fall. In treated vines, a constantly pressurised low-pressure in situ trunk injection system (69 kPa) with 5% w/v sucrose solution over 45 days from budburst (to flowering), in each of the two seasons, delivered a widely variable amount of sucrose into each vine with variation ascribed to the amount of internal dead wood in the trunk. In the best circumstances, an average of 150 g sucrose/vine/season was injected, and sucrose-injected vines had higher trunk TNC reserve (4.1% compared to 3.6% w/dw in the control) at flowering. A δ13C (‰) analysis confirmed the presence of injected sucrose in the shoot at flowering. However, the correlation between the amount of loaded sucrose and δ13C in young shoot tissue was poor, indicative of variable partitioning patterns. Inflorescence number per vine and berry yield were markedly higher in sucrose-injected vines, but differences were not significant given the high variation between vines. The addition of KCl to the sucrose solution and use of the healthy vines are recommended to increase sucrose loading using the injection method to address inconsistent yielding of subtropical table grape.

A self-supervised overlapped multiple weed and crop leaf segmentation approach under complex light condition

A self-supervised overlapped multiple weed and crop leaf segmentation approach under complex light condition

Impact of irrigation and nitrogen management on crop performance, yield and economics of sorghum (<i>Sorghum bicolor</i>) inKandahar region of Afghanistan

In Afghanistan the information on the performance of sorghum crop to applied nitrogen (N) fertilizer coupled with water management are still scarce. Therefore, a field study was carried out during rainy (kharif) season of 2020 at the research farm of Afghanistan National Agricultural Science and Technology University, Kandahar, Afghanistan. Kandahar in collaboration with ICAR-Indian Agricultural Research Institute, New Delhi evaluated the effect of different irrigation scheduling and nitrogen management options on growth, yield and economics of sorghum. The experiment was laid out in a split-plot design with 3 replications consisting of 3 irrigation schedules in main-plots and 4-N rates in sub-plots. Growth parameters of sorghum, viz. plant height, dry matter accumulation, crop growth rate and leaf area index were significantly increased by N120 and N80 compared to N0 plots. Significantly superior yields, viz. seed yield (1546 and 1476 kg/ha), straw yield (5546 and 5467 kg/ha), biological yield (7092 and 6943 kg/ha) and harvest index (21.3 and 21.8%) were observed with N120 and N80 plots, respectively. The N120 and N80 plots remained at par with each other with respect to net B:C ratio (1.87). However, the highest production efficiency (14.7 kg biomass/day) and monetary efficiency (773.90 AFN/day) was obtained with N120 plots. Therefore, the findings of present study suggest that N application @80 kg/ha could be a viable option to attain higher productivity, farm profitability and input-use efficiency irrespective of 2 or 3 irrigations in sorghum in Kandahar region of Afghanistan.

A novel ensemble learning method for crop leaf disease recognition.

Deep learning models have been widely applied in the field of crop disease recognition. There are various types of crops and diseases, each potentially possessing distinct and effective features. This brings a great challenge to the generalization performance of recognition models and makes it very difficult to build a unified model capable of achieving optimal recognition performance on all kinds of crops and diseases. In order to solve this problem, we have proposed a novel ensemble learning method for crop leaf disease recognition (named ELCDR). Unlike the traditional voting strategy of ensemble learning, ELCDR assigns different weights to the models based on their feature extraction performance during ensemble learning. In ELCDR, the models' feature extraction performance is measured by the distribution of the feature vectors of the training set. If a model could distinguish more feature differences between different categories, then it receives a higher weight during ensemble learning. We conducted experiments on the disease images of four kinds of crops. The experimental results show that in comparison to the optimal single model recognition method, ELCDR improves by as much as 1.5 (apple), 0.88 (corn), 2.25 (grape), and 1.5 (rice) percentage points in accuracy. Compared with the voting strategy of ensemble learning, ELCDR improves by as much as 1.75 (apple), 1.25 (corn), 0.75 (grape), and 7 (rice) percentage points in accuracy in each case. Additionally, ELCDR also has improvements on precision, recall, and F1 measure metrics. These experiments provide evidence of the effectiveness of ELCDR in the realm of crop leaf disease recognition.

Determinants of the accuracy of using carbon isotopes in estimating water use efficiency of selected cereal and legume crops: A global perspective

AbstractField assessments of crop water use efficiency (WUE) are resource‐consuming since they require simultaneous assessment of the total amount of water assimilated by crops for biomass and/or grain production. Alternative methods exist, such as estimating the carbon isotopic ratio (13C/12C) of the crop's leaf, aboveground biomass, or grain samples. There is limited information on the determinants of the accuracy of carbon isotopes in estimating water use efficiency between crop types and environments. Therefore, this study aimed to evaluate the extent to which the estimation of the 13C/12C ratio in crop parts constitutes an accurate proxy of WUE, globally. Data on observed WUE (WUEobs) were collated involving 518 experiments conducted worldwide on major cereals and legumes and compared with WUE estimates (WUEest) from carbon isotopes. The mean WUEobs among all experiments was 3.4 g L−1 and the mean absolute error (MAE) was 0.5 g L−1 or 14.7% of WUEobs, corresponding to accurate predictions at p < 0.05. However, the percentage mean absolute error of observed water use efficiency (%MAE) estimated from grains was 3.6 ± 11.5%, which was lower than the %MAE from aboveground biomass collected at harvest (3 ± 22.8%). In addition, the %MAE increased from 1.1 ± 5.1% for soybean, 1.6 ± 7.2% for maize, 1.2 ± 8.6% for rice, 1.8 ± 12.1% for groundnut, 2.1 ± 14.3% for cowpea, 2.3 ± 16.2% for bush bean, 1.8 ± 19.9% for wheat, 2.2 ± 21.4% for barley to 6.3 ± 39.3% for oat, with only the latter corresponding to significant errors. WUEest were, in all cases, unbiased but slightly overestimated from 0.8% (maize) to 15.4% (oat). The accuracy in estimating WUE significantly decreased with the increase in soil clay content, with sand, showing a positive correlation of 0.3 with %MAE, but negatively correlated with the silt content (r = −0.4). Furthermore, a multivariate analysis pointed out a tendency for prediction errors and bias to increase with the decrease in WUEobs and air temperature. Using carbon isotopes for estimating crop WUE thus appeared reliable for all crops and world environments, provided grain samples are considered. The technique tended to perform better under high WUE conditions, such as those generally found in maize and soybean cropping systems. The identified factors that affect the accuracy of using carbon isotopes in measuring WUE provide valuable insights for water resource management and sustainable crop production. These findings contribute to the ongoing discourse on water conservation strategies in agriculture, offering a basis for decision‐making in crop improvement programs. Implementing the recommended practices from this study can potentially improve yield gains and promote resilient and sustainable agricultural systems in the changing environmental circumstances. Further research should investigate the mechanisms that cause low accuracy of the isotopic technique using aboveground biomass and under arid and cool environments.

Effect of foliar supplementation of water soluble fertilizer on growth, yield and economics of wheat crop (Triticum aestivum L.)

A field experiment was conducted during rabi season of 2015-16 at Agronomy Research Farm of Chaudhary Charan Singh Haryana Agricultural University, Hisar, Haryana, to evaluate the effects of foliar application of water soluble fertilizers on growth, yield attributes and yield of wheat. The soil of the experimental plot was sandy loam in texture, low in available nitrogen (131.3 kg ha-1), medium in available phosphorus (17.2 kg ha-1), and high in available potassium (335.4 kg ha-1). The soil was alkaline in reaction (pH 7.8). The experiment was laid out in randomized block design with 17 treatments having three replications. The treatments comprising two fertilizers level i.e., Recommended dose of fertilizer (RDF) (150 kg N + 60 kg P2O5 + 60 kg K2O ha-1) and 75% of RDF and supplemented with foliar application of water soluble fertilizers viz. Urea phosphate (17:44) @ 1.5%, NPK (18:18:18) @ 1.5%, ZnSO4 @ 0.5% and Urea 2% at different stages. One treatment was control where no fertilizer applied. Growth parameters viz, plant height, dry matter accumulation, crop growth rate and leaf area index were increased with T9 i.e., RDF along with foliar application of water soluble fertilizers NPK (18:18:18) @ 1.5% at 40 DAS followed by Urea phosphate (17:44) @ 1.5% at 65 DAS. Among treatments, application of RDF along with foliar application of water soluble fertilizers NPK (18:18:18) @ 1.5% at 40 DAS followed by Urea phosphate (17:44) @ 1.5% at 65 DAS exhibited the highest values of yield attributes, viz. effective tillers/ meter row length (mrl), number of spikeletes/spike, number of grains/spike and 1000-grains weight which led to the highest grain yield (6220 kg ha-1) However, alternate application of NPK (18:18:18) @ 1.5% and Urea phosphate (17:44) @ 1.5% or vice versa produced statistically similar grain yield of wheat at the same level of fertilizer application. RDF or 75% RDF along with foliar application of NPK (18:18:18) @ 1.5% was slightly superior as compared to RDF or 75% RDF along with foliar application of Urea phosphate (17:44) @ 1.5% irrespective of time of application.

An evaluation of Goudriaan’s summary model for light interception in strip canopies, using functional-structural plant models

Abstract Dealing with heterogeneity in leaf canopies when calculating light interception per species in a mixed canopy is a challenge. Goudriaan developed a computationally simple, though conceptually sophisticated, model for light interception in strip canopies, which can be reasonably represented as ‘blocks’, such as vineyards and crop rows. This model is widely used, but there is no independent verification of the model. Hence, we developed a comparison of light interception calculations with Goudriaan’s model and with detailed spatially explicit three-dimensional functional–structural plant models (FSPM) of maize in which plant architecture can be represented explicitly. Two models were developed, one with small randomly oriented leaves in blocks, similar to Goudriaan’s assumption, which we refer to as the intermediate model (IM), and another with a realistic representation of individual plants with stems and leaves having shape, orientation and so on, referred as FSPM. In IM and FSPM, light interception was calculated using ray tracing. In Goudriaan’s model, the light extinction coefficient (k), including both its daily and seasonal average values, was generated using the FSPM. Correspondence between the three models was excellent in terms of light capture for different levels of crop height, leaf area and uniformity, with the difference less than 3.3 %. The results are strong support for the use of Goudriaan’s summary model for calculating light interception in strip canopies.

Deep Convolutional Neural Networks Fusion with Support Vector Machines and K-Nearest Neighbors for Precise Crop Leaf Disease Classification

Deep Convolutional Neural Networks Fusion with Support Vector Machines and K-Nearest Neighbors for Precise Crop Leaf Disease Classification

Crops Leaf Disease Recognition From Digital and RS Imaging Using Fusion of Multi Self-Attention RBNet Deep Architectures and Modified Dragonfly Optimization

Crops Leaf Disease Recognition From Digital and RS Imaging Using Fusion of Multi Self-Attention RBNet Deep Architectures and Modified Dragonfly Optimization