The identification of compounds was conducted by determining the peak area of the absorbance at 520 nm for anthocyanins and made by comparison of the commercial standard retention times found in the literature. Commercial standard of oenin was used for the quantification of anthocyanins.Much of the literature regarding competition between cover crop and grapevine suggested that the presence of a cover crop increased the competition for nutrients, namely nitrogen . Although it was hypothesized that RV and AG would decrease grapevine mineral nutrition status, there were no effects of cover crop on grapevine mineral nutrition in the present study. In Fresno, the nutrient status of the grapevines at bloom only differed between years and was not affected by treatments, indicating little to no competition with the cover crop for nutrients in mature vineyards under arid conditions. However, in Oakville tillage appeared to be minimally impactful on grapevine mineral nutrition, and CT vines demonstrated a higher nitrogen content at bloom compared to NT in both years. Higher N in both leaves and juice in response to tillage was previously reported . In one particular three-year study, NO3- Npetiole values of grapevines with interrow tillage were found to be up to 2 × greater than those of no-till grapevines,round plastic pot suggesting a possible temporal offset between soil N availability and plant uptake related to tillage .
It is well demonstrated that soil tillage affects the decomposition and mineralization of N from plant residues and existingpool which regulates the inorganic N pool available for uptake by the grapevines . Thus, while the type of cover crop did not influence nitrogen content in the present study, nor was there an interaction between cover crop and tillage. It was also hypothesized that PG would improve grapevine water status due to spatial and temporal complementarity, whereby the shallow rooting depth would be less likely to compete with grapevines and peak water use of PG would occur during grapevine dormancy. In Fresno, no treatment effects were observed on grapevine water status nor among WF components. This result is particularly important as it indicated that despite different growth habits of the cover crop , there was no competition with the grapevines for water, as also indicated by a lack of differences in Ψs between treatments. In Oakville, PG did indeed improve grapevine water status compared to RV and AG in one instance during 2020, but this effect was not observed in the second year of the study, nor over the two seasons. Furthermore, there was no interaction effect between the PG cover crop and NT factor, which is particularly important as the greatest benefits to the soil from a permanent cover crop were observed under no-till environments . While the type of cover crop again had little influence, CT improved grapevine water status compared to grapevines under NT. Although Steenwerth et al. previously reported reduced soil water content under no-till settings, no association was found with ΨS. While contradictory to some reports that indicated tillage did not affect grapevine water status , this result provided further evidence of tillage in semi-arid regions to preserve water in the soil through early season cultivation.
This was based on the notion that while evaporative losses of the upper tilled layer of soil immediately increase, overall losses are minimized as a barrier that restricts capillary water movement is created which preserves moisture in the deeper layers of the soil . Furthermore, seasonal grapevine water status in irrigated viticulture was shown to be more influenced with subsoil conditions than the topsoil which dries quite early . As the presence of vegetation was shown to deplete water out of the upper portion of the soil more rapidly than bare soil , it is possible that the complete termination and incorporation of vegetation helped preserved moisture in the soil compared to NT, where vegetation was able to remain in competition with the grapevines. However, no conclusions can be made as to the mechanism of reduced water stress under CT vines as root structure was not examined in the present study . Ultimately, these differences in plant water status between tillage systems in Oakville did not affect WF components, as irrigation amounts remained unchanged.In regard to grapevine yield and berry composition, most differences were observed between years at the Fresno vineyard. Although no yield components were affected by cover crop or tillage, minimal effects of tillage on berry composition were seen. The TA was significantly higher in CT compared to NT in both years, as has been previously reported when permanent grass was compared to conventionally tilled soil . This may suggest that tillage hastens the ripening process, however, no statistically significant effects were observed on TSS which would support such a claim .
Other studies that investigated the influence of vineyard floor management in mature vineyards reported similar findings with reduced effect of soil management practices as grapevines aged. It was possible that mature grapevines may be more resilient to the of the adoption of cover crops due to their well-established root systems that can more effectively compete with the cover crop . Combined, these results provided evidence that the use of the annual or perennial grass cover crops and/or no-till practices may be implemented in mature irrigated vineyards in the San Joaquin Valley with little to no effect on production. In the Oakville vineyard, TA was again significantly higher under CT compared to NT in 2021. However, this effect was only seen in the second year of the study resulting in a year by tillage interaction without a significant main effect. The juice pH was also reduced under CT grapevines, which has been a reported effect of cover crop adoption rather than tillage as a result of the release of potassium when the cover crop decomposed . While several studies have reported no reductions to yield in response to cover crops, others that assessed permanent cover crops observed decreased yield after 2 to 3 years . It is not clear whether competition for water or N was the primary cause as the two factors are interconnected . The absence of effect on yield, as seen in this study, may be a result of the shorter length of experiment, and/or shallower rooting depth of the perennial grass used compared to deeper rooting and higher biomass producing grasses investigated in the aforementioned studies. Ultimately, the adoption of cover crops under both tillage systems in the present study did not affect production despite great differences in soil type, vineyard age, and climate between the two sites.While the benefits of cover crops and reduced tillage on soil physical, chemical, and biological properties are well documented, the link between these management practices and grapevine physiology, components of yield and berry composition remain unclear. At both sites, there were no treatment effects on leaf gas exchange which suggested negligible effect of cover crop and tillage on grapevine physiology in vineyards of these climates. In the mature Ruby Cabernet vineyard in Fresno, no changes to yield components or berry composition among cover crop andtillage were measured. In the young Merlot vineyard in Oakville, NT detrimentally affected grapevine water status. This indicated that the presence of vegetation in the early spring increased competition for water, but ultimately no changes to yield or water footprint were observed. Our results provided evidence that both in mature and young vineyards cover cropping had negligible beneficial effects on grapevine physiology,round pot mineral nutrition or production; and tillage was beneficial in young vineyards to improve plant water status in semi-arid regions. As the global population continues to expand, “it is estimated that food production will need to increase by 60% by 2050 to feed the estimated 10 billion people expected on Earth. An increase in production along with a reduction in food loss due to pests and pathogens and food waste will be needed to meet demand” . Crop loss resulting from plant diseases and pests poses a formidable challenge for crop growers worldwide. Plant diseases and pests lower the product quality or shelf-life of crops, decrease the nutritional value of vegetables and fruits, and reduce crop yield. The Food and Agriculture Organization of the United Nations estimates that annually 20% to 40% of global crop production are lost to pests. Each year, plant diseases cost the global economy around $220 billion USD, and invasive insects around $70 billion US. A challenge crop growers face is accurately identifying the disease responsible for their crop losses. The identification process is particularly challenging as some plant diseases exhibit similar symptoms, particularly during the early stages of infection.
Consequently, discerning the nuanced distinctions becomes a daunting task for the human eye. Often, crop growers can recognize the disease after it has significantly affected their crops or when the infection or infestation has persisted over a prolonged period of time, leading to observable alterations in leaf appearance or crop loss. It is crucial to emphasize the significance of proper disease identification, as employing the wrong treatment can be a waste of time, financial resources, and possibly cause further crop loss or damage. In order to facilitate the identification of plant diseases, Mohanty et al. proposed a novel approach in their scholarly work titled “Using Deep Learning for Image-Based Plant Disease Classification”. The researchers explored the utilization of deep learning convolutional neural network models to effectively discern various types of plant diseases. The data set in their study was obtained from the PlantVillage project, encompassing a vast collection of 54,306 color images depicting 14 distinct crop species afflicted with 20 different disease types or healthy conditions. The authors conducted an extensive investigation, comparing the effectiveness of using color images versus gray-scale and segmented images, exploring various training validation-testing splits, comparing the outcomes of training models from scratch versus utilizing pre-trained models, and evaluating the performance of GoogLeNet and AlexNet, two different deep learning convolutional neural network architectures. Through the systematic exploration of these factors, they conducted a total of 60 experiments to ascertain the optimal combination of architectural configurations. While the study from authors Mohanty et al. primarily focused on deep convolutional neural networks, subsequent research has demonstrated that convolutional neural networks are not the sole approach to achieving excellent performance in image classification tasks. These claims come from the paper “An Image is worth 16X16 Words: Transformers for Image Recognition at Scale” by Alexey Dosovitskiy et al,. Their paper finds that employing a pure transformer applied directly to sequences of images, when pre-trained on substantial volumes of data and transferred to multiple mid-sized or small image recognition benchmarks such as ImageNet, CIFAR-100, or VTAB, can yield highly competitive outcomes. The Vision Transformer architecture has showcased remarkable performance compared to state-of-the-art convolutional networks, while also significantly reducing the computational resources required for training. Consequently, the motivation for this project is to loosely follow the framework outlined in the study by Mohanty et al,. However, instead of employing a deep convolutional network architecture, a Vision Transformer model pre-trained on the ImageNet-21k data set will be utilized to implement transfer learning and to train a disease classification model with the project PlantVillage data set. The data for this project is the project PlantVillage data which was found through the paper, “Using Deep Learning for Image-Based Plant Disease Detection” . The data consists of 54,306 color images of healthy and diseased crop leaves. In a machine learning sense, our data set of 54,306 images is considered small. Each image is the size of 256 × 256 pixels, has the three color channels RGB, and is categorized under a CropDisease Classification Label. Each label has a crop species name and a plant disease name or healthy. There are 14 different crop species and 20 different crop diseases, which create 38 different crop-disease Classification Labels in this data set. See Table 2.1 for a detailed list of all Classification Labels. Table 2.1 shows the total number of images each Classification Label contains and how that amount is translated to be the overall percentage contribution to the data set. Classification labels with over 5,000 images had the largest percent contributions to the data set. These labels are Orange-Haunglongbing with 10.1%, Tomato-Yellow Leaf Curl with 9.9%, and Soybean-Healthy with 9.4%. The classification labels with less than 500 images and having the least amount of percent contributions to the data set are Peach-Healthy, Raspberry-Healthy, and Tomato-Mosaic Virus, all with 0.7%, Apple-Cedar Apple Rust with 0.5%, and Potato-Healthy with 0.3%.