An Improvement of Yield Production Rate for Crops by Predicting Disease Rate using Intelligent Decision System

Agriculture is the country's mainstay. Plant diseases reduce production and thus product prices. Clearly, prices of edible and non-edible goods rose dramatically after the outbreak. We can save plants and correct pricing inconsistencies using automated disease detection. Using light detection and range (LIDAR) to identify plant diseases lets farmers handle dense volumes with minimal human intervention. To address the limitations of passive systems like climate, light variations, viewing angle, and canopy architecture, LIDAR sensors are used. The DSRC was used to receive an alert signal from the cloud server and convey it to farmers in real-time via cluster heads. For each concept, we evaluate its strengths and weaknesses, as well as the potential for future research. This research work aims to improve the way deep neural networks identify plant diseases. Google Net, Inceptionv3, Res Net 50, and Improved Vgg19 are evaluated before Biased CNN. Finally, our proposed Biased CNN (B-CNN) methodology boosted farmers' production by 93% per area.

3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves

Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.

Contrasting Carbon Allocation Strategies of Ring-Porous and Diffuse-Porous Species Converge Toward Similar Growth Responses to Drought

Extreme climatic events that are expected under global warming expose forest ecosystems to drought stress, which may affect the growth and productivity. We assessed intra-annual growth responses of trees to soil water content in species belonging to different functional groups of tree-ring porosity. We pose the hypothesis that species with contrasting carbon allocation strategies, which emerge from different relationships between wood traits and canopy architecture, display divergent growth responses to drought. We selected two diffuse-porous species (Acer saccharum and Betula alleghaniensis) and two ring-porous species (Quercus rubra and Fraxinus americana) from the mixed forest of Quebec (Canada). We measured anatomical wood traits and canopy architecture in eight individuals per species and assessed tree growth sensitivity to water balance during 2008–2017 using the standardized precipitation evapotranspiration index (SPEI). Stem elongation in diffuse-porous species mainly depended upon the total number of ramifications and hydraulic diameter of the tree-ring vessels. In ring-porous species, stem elongation mainly depended upon the productivity of the current year, i.e., number of vessels and basal area increment. Diffuse-porous and ring-porous species had similar responses to soil water balance. The effect of soil water balance on tree growth changed during the growing season. In April, decreasing soil temperature linked to wet conditions could explain the negative relationship between SPEI and tree growth. In late spring, greater water availability affected carbon partitioning, by promoting the formation of larger xylem vessels in both functional groups. Results suggest that timings and duration of drought events affect meristem growth and carbon allocation in both functional groups. Drought induces the formation of fewer xylem vessels in ring-porous species, and smaller xylem vessels in diffuse-porous species, the latter being also prone to a decline in stem elongation due to a reduced number of ramifications. Indeed, stem elongation of diffuse-porous species is influenced by environmental conditions of the previous year, which determine the total number of ramifications during the current year. Drought responses in different functional groups are thus characterized by different drivers, express contrasting levels of resistance or resilience, but finally result in an overall similar loss of productivity.

Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling

Berry cell death assessment can become one of the most objective parameters to assess important berry quality traits, such as aroma profiles that can be passed to the wine in the winemaking process. At the moment, the only practical tool to assess berry cell death in the field is using portable near-infrared spectroscopy (NIR) and machine learning (ML) models. This research tested the NIR and ML approach and developed supervised regression ML models using Shiraz and Chardonnay berries and wines from a vineyard located in Yarra Valley, Victoria, Australia. An ML model was developed using NIR measurements from intact berries as inputs to estimate berry cell death (BCD), living tissue (LT) (Model 1). Furthermore, canopy architecture parameters obtained from cover photography of grapevine canopies and computer vision analysis were also tested as inputs to develop ML models to assess BCD and LT (Model 2) and the intensity of sensory descriptors based on visual and aroma profiles of wines for Chardonnay (Model 3) and Shiraz (Model 4). The results showed high accuracy and performance of models developed based on correlation coefficient (R) and slope (b) (M1: R = 0.87; b = 0.82; M2: R = 0.98; b = 0.93; M3: R = 0.99; b = 0.99; M4: R = 0.99; b = 1.00). Models developed based on canopy architecture, and computer vision can be used to automatically estimate the vigor and berry and wine quality traits using proximal remote sensing and with visible cameras as the payload of unmanned aerial vehicles (UAV).

Quantitative Survey of ‘Montmorency’ Tart Cherry Orchard Design in Utah

‘Montmorency’ tart cherry trees (Prunus cerasus L.) are grown commercially in the United States in low-density systems. Commercial tart cherry orchard design has not changed significantly over the past 50 years, but there is some variation from farm to farm in management strategies, including tree spacing, training, and pruning, and the resulting orchard production and turnover. Canopy dimensions and dynamics are important considerations for evaluating and improving orchard management strategies but are not well documented for tart cherry systems. Current orchard design and canopy management strategies were surveyed along a gradient of orchard age across five commercial farming operations in Utah. Trunk cross-sectional area and various canopy dimensions, including spread and volume, were quantified to capture tree size and canopy architecture. The survey indicated a surprising lack of deviation in orchard design in the region over the last several decades with higher variation among blocks within a farm than across farms. As a result, the survey revealed trends in tree growth and canopy structure across the range in orchard ages despite differences in management approaches of the surveyed farms. These trends were useful in illustrating canopy development and space fill. Tree age between 11 and 15 years after planting was determined to represent a transition between establishment and mature growth, where canopies filled available row space and began experiencing senescing canopy structure. Based on the distribution of ages captured in the survey, a significant number of orchards in Utah are at an age range of 11–15 years, perhaps contributing to superior yields per land area reported for the region. The confluence of space-fill and canopy development described in this study highlights a critical period for tart cherry orchard management at the transition of canopy establishment and maturity. These baseline dynamics will provide benchmarks for evaluating strategies for refining and improving orchard management systems for tart cherry in the Intermountain West region.

Optimizing Peach Tree Canopy Architecture for Efficient Light Use, Increased Productivity and Improved Fruit Quality

Peach production in the USA has been in decline in recent decades due to poor fruit quality, reduced consumption and increased cost of production. Productivity and fruit quality can only be enhanced in the orchard through optimizing preharvest factors such as orchard design and training systems. Transition from low-density plantings (LDP) to high-density plantings (HDP) in peach is associated with the availability of reliable size controlling rootstocks. Increased densities must be combined with modern training systems to diffuse vigor and further increase light interception and yields, while optimizing light distribution, fruit quality and cost of production. Several training systems have been tested in peach with various objectives and goals, such as increasing light, water use and labor efficiencies, along with designing canopy architectures to facilitate mechanization and robotics. In general, increased planting densities increase yields, but excessive densities can promote shade, while excessive crop load can deteriorate quality. An ideal peach cropping system should optimize light interception and light distribution to balance maximum yield potential with maximum fruit quality potential. Successful management of high-density peach fruiting wall systems can lead to enhanced and uniform fruit quality, and ensure a sustainable industry.