Big Data analytics for Advanced Viticulture

Big data analytics involve systematic approach to find hidden patterns to help the organization grow from large volume and variety of data. In recent years big data analytics is widely used in the agricultural domain to improve yield. Viticulture (the cultivation of grapes) is one of the most lucrative farming in India. It is a subdivision of horticulture and is the study of wine growing. The demand for Indian Wine is increasing at about 27% each year since the 21st century and thus more and more ways are being developed to improve the quality and quantity of the wine products. In this paper, we focus on a specific agricultural practice as viticulture. Weather forecasting and disease detection are the two main research areas in precision viticulture. Leaf disease detection as a part of plant pathology is the key research area in this paper. It can be applied on vineyards of India where farmers are bereft of the latest technologies. Proposed system architecture comprises four modules: Data collection, data preprocessing, classification and visualization. Database module involve grape leaf dataset, consists of healthy images combined with disease leaves such as Black measles, Black rot, and Leaf blight. Models have been implemented on Apache Hadoop using map reduce programming framework. It apply feature extraction to extract various features of the live images and classification algorithm with reduced computational complexity. Gray Level Co-occurrence Matrix (GLCM) followed by K-Nearest Neighborhood (KNN) algorithm. System also recommends the necessary steps and remedies that the viticulturists can take to assure that the grapes can be salvaged at the right time and in the right manner based on classification results. Overall system will help Indian viticulturists to improve the harvesting process. Accuracy of the model is 72% and it can be increased as a future work by including deep learning with time series grape leaf images.

Fused-Deep-Features Based Grape Leaf Disease Diagnosis

Rapid and accurate grape leaf disease diagnosis is of great significance to its yield and quality of grape. In this paper, aiming at the identification of grape leaf diseases, a fast and accurate detection method based on fused deep features, extracted from a convolutional neural network (CNN), plus a support vector machine (SVM) is proposed. In the research, based on an open dataset, three types of state-of-the-art CNN networks, three kinds of deep feature fusion methods, seven species of deep feature layers, and a multi-class SVM classifier were studied. Firstly, images were resized to meet the input requirements of the CNN network; then, the deep features of the input images were extracted via the specific deep feature layer of the CNN network. Two kinds of deep features from different networks were then fused using different fusion methods to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused deep features. The experimental results on the open dataset show that the fused deep features with any kind of fusion method can obtain a better classification performance than using a single type of deep feature. The direct concatenation of the Fc1000 deep feature extracted from ResNet50 and ResNet101 can achieve the best classification result compared with the other two fusion methods, and its F1 score is 99.81%. Furthermore, the SVM classifier trained using the proposed method can achieve a classification performance comparable to that of using the CNN model directly, but the training time is less than 1 s, which has an advantage over spending tens of minutes training a CNN model. The experimental results indicate that the method proposed in this paper can achieve fast and accurate identification of grape leaf diseases and meet the needs of actual agricultural production.

Fine-Grained Grape Leaf Diseases Recognition Method Based on Improved Lightweight Attention Network

Real-time dynamic monitoring of orchard grape leaf diseases can greatly improve the efficiency of disease control and is of great significance to the healthy and stable development of the grape industry. Traditional manual disease-monitoring methods are inefficient, labor-intensive, and ineffective. Therefore, an efficient method is urgently needed for real-time dynamic monitoring of orchard grape diseases. The classical deep learning network can achieve high accuracy in recognizing grape leaf diseases; however, the large amount of model parameters requires huge computing resources, and it is difficult to deploy to actual application scenarios. To solve the above problems, a cross-channel interactive attention mechanism-based lightweight model (ECA-SNet) is proposed. First, based on 6,867 collected images of five common leaf diseases of measles, black rot, downy mildew, leaf blight, powdery mildew, and healthy leaves, image augmentation techniques are used to construct the training, validation, and test set. Then, with ShuffleNet-v2 as the backbone, an efficient channel attention strategy is introduced to strengthen the ability of the model for extracting fine-grained lesion features. Ultimately, the efficient lightweight model ECA-SNet is obtained by further simplifying the network layer structure. The model parameters amount of ECA-SNet 0.5× is only 24.6% of ShuffleNet-v2 1.0×, but the recognition accuracy is increased by 3.66 percentage points to 98.86%, and FLOPs are only 37.4 M, which means the performance is significantly better than other commonly used lightweight methods. Although the similarity of fine-grained features of different diseases image is relatively high, the average F1-score of the proposed lightweight model can still reach 0.988, which means the model has strong stability and anti-interference ability. The results show that the lightweight attention mechanism model proposed in this paper can efficiently use image fine-grained information to diagnose orchard grape leaf diseases at a low computing cost.

Occurrence of Fungicides in Vineyard and the Surrounding Environment

Seventeen fungicides were determined in different matrices from vineyard areas, including vine leaves, soils, grapes and water, using gas chromatography coupled to tandem mass spectrometry (GC-MS/MS). For leaf analysis, ultrasound-assisted extraction (UAE) was performed evaluating different solvents. UAE was compared with other extraction techniques such as vortex extraction (VE) and matrix solid-phase dispersion (MSPD). The performance of the UAE method was demonstrated on vine leaf samples and on other types of samples such as tea leaves, underlining its general suitability for leaf crops. As regards other matrices, soils were analyzed by UAE and microwave-assisted extraction (MAE), grapes by UAE and waters by SPE using cork as the sorbent. The proposed method was applied to 17 grape leaf samples in which 14 of the target fungicides were detected at concentrations up to 1000 μg g−1. Furthermore, the diffusion and transport of fungicides was demonstrated not only in crops but also in environmental matrices.

Molecular Evaluation of Kyoho Grape Leaf and Berry Characteristics Influenced by Different NPK Fertilizers

Fertilization, a fundamental aspect of a plant’s life, has been of great concern for agricultural specialists to minimize the yield gap between actual and potential yield. Around the globe, fertilizers with different NPK ratios are being used to attain a better yield of grape. To find the suitable commercially available fertilizer for quality grape production, a 2 years (2017–2018) study was conducted for the evaluation of 10 fertilizers with different NPK ratios. Commercial fertilizers included were Zhanlan (16:16:16), Garsoni (15:15:15), Acron (16:16:16), Norway (21:7:12), Peters 1 (30:10:10), Nutrivant (14:14:30), Peters 2 (20:20:20), UMAX (15:15:15), G2 (20:20:20), and Yara (15:15:15). The fertilizer application rate was 20 g plant−1, and each was applied at L-29, L-33, and L-36 phenological stages. Chlorophylls, carotenoids, macro/micronutrients in leaf, and anthocyanin derivatives in grape peel were evaluated. Expression levels of 24 genes, including nitrogen, phosphorous, potassium, and anthocyanin pathways in leaf, peel, and pulp were validated by qPCR at L-29, L-33, and L-36 stages. Results indicated that Norway (21:7:12) and Peters 1 (30:10:10) increased carotenoids, chlorophylls, and anthocyanins in leaves, while Zhanlan (16:16:16) improved fruit biochemical attributes, and anthocyanin (cyanidin, delphinidin, petunidin, malvidin, peonidin, and pelargonidin contents). However, a better grape yield was obtained by the application of Peters 1 (30:10:10). Potassium pathway genes were upregulated by Nutrivant (14:14:30), phosphorous pathway genes by Peters 2 (20:20:20), and nitrogen pathway genes by Peters 1 (30:10:10), while Nutrivant (14:14:30) upregulated anthocyanin pathway genes and simultaneously enhanced anthocyanin biosynthesis in berry peels. Results of two years’ study concluded that Peters 1 (30:10:10) was proved better to increase yield, while Zhanlan (14:14:30) was superior in improving anthocyanin biosynthesis.

Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning

The disease spots on the grape leaves can be detected by using the image processing and deep learning methods. However, the accuracy and efficiency of the detection are still the challenges. The convolutional substrate information is fuzzy, and the detection results are not satisfactory if the disease spot is relatively small. In particular, the detection will be difficult if the number of pixels of the spot is <32 × 32 in the image. In order to effectively address this problem, we present a super-resolution image enhancement and convolutional neural network-based algorithm for the detection of black rot on grape leaves. First, the original image is up-sampled and enhanced with local details using the bilinear interpolation. As a result, the number of pixels in the image increase. Then, the enhanced images are fed into the proposed YOLOv3-SPP network for detection. In the proposed network, the IOU (Intersection Over Union, IOU) in the original YOLOv3 network is replaced with GIOU (Generalized Intersection Over Union, GIOU). In addition, we also add the SPP (Spatial Pyramid Pooling, SPP) module to improve the detection performance of the network. Finally, the official pre-trained weights of YOLOv3 are used for fast convergence. The test set test_pv from the Plant Village and the test set test_orchard from the orchard field were used to evaluate the network performance. The results of test_pv show that the grape leaf black rot is detected by the YOLOv3-SPP with 95.79% detection accuracy and 94.52% detector recall, which is a 5.94% greater in terms of accuracy and 10.67% greater in terms of recall as compared to the original YOLOv3. The results of test_orchard show that the method proposed in this paper can be applied in field environment with 86.69% detection precision and 82.27% detector recall, and the accuracy and recall were improved to 94.05 and 93.26% if the images with the simple background. Therefore, the detection method proposed in this work effectively solves the detection task of small targets and improves the detection effectiveness of the grape leaf black rot.

Effect of Adding Different Levels of Grapes (Vitis vinifera L.) Seeds and Leaf Powder or their Extracts on Some Bone Characteristics and Total Ash Content in Broiler Chickens

Background: Some natural sources of polyphenols like grape seed, leaf or extracts, have many benefits in maintaining bone health in animals. This study aimed to investigate the efficacy of grape seeds, grape leaves powder, or their extracts on some bone characteristics and total ash content in broiler chickens. Methods: Three hundred and twenty four one-day-old broilers were allocated to nine treatments with three replicates containing 12 chicks each. Chicks were fed nine experimental diets for 35 days; as a control diet without supplementation (T1), control diets supplemented with 15 and 30 g/kg grape seeds powder (GSP) (T2, T3), 15 and 30 g/kg grape leaf powder (GLP) (T4, T5), grape seed extract (GSE) at levels 2 and 4 ml/l added in drinking water (T6, T7) and with grape leaf extract (GLE ) at levels 2 and 4 ml/l (T8 and T9), respectively. Result: Significant increase (p≤0.05) in the bone length was recorded with GSP (30 g/kg), GLP (15 g/kg), GSE or GLE and calcium percent with GLE as compared with control. Furthermore, significant improvement was recorded in predictive skeletal weight of broilers fed GSE (T6 α T7) as compared to the other treatments. The study concluded that the best results were achieved at the 2 or 4 ml/l of grape seed extract in improving femur length, predicted skeletal weight and from grape leaf extract in improving calcium percentage in broiler bone ash.