Priority selection of agro-meteorological parameters for integrated plant diseases management through analytical hierarchy process

To understand the influence of agro-meteorological parameters to take decisions related to various factors in an integrated plant disease management, it becomes vital to carry out scientific studies on the factors affecting it. The different agro-meteorological parameters namely temperature, humidity, moisture, rain, phenological week, cropping season, soil type, location, precipitation, heat index, and cloud coverage have been considered for this study. Each parameter has been allocated the ranking by using a technique called analytical hierarchical process (AHP). The parameter priorities are determined by calculating the Eigenvalues. This helps to make decisions related to integrated plant disease management where the prediction of plant disease occurrence, yield prediction, irrigation requirements, and fertilization recommendations can be taken. To take these decisions which parameters are good indicators can be identified using this method. The parameters majorly contribute to plant diseases and pest management decision making while delivers minor contribution in irrigation and fertilizer management related decision making. The manual results are compared with software generated results which indicates that both the results correlate with each other. Therefore, AHP technique can be successfully implemented for prioritizing agro-meteorological parameters for integrated plant diseases management as the results for both levels are consistent (consistency ratio < 0.1).

​Antagonistic Activity of Panchagavya and Trichoderma spp. against Wilt Complex causing Pathogens of Chickpea (Cicer arietinum L.)

Background: Chickpea wilt complex caused by several soil-borne pathogens is the major yield-reducing malady worldwide. Biological control is one of the best, low-cost and ecologically sustainable method for managing plant diseases caused by soil-borne pathogens. Methods: In this present investigation Panchagavya and Trichoderma spp. were evaluated by following poisoned food technique and dual culture technique against wilt complex causing pathogens i.e. Fusarium oxysporum f. sp. ciceri, Fusarium solani and Macrophomina phaseolina. Result: Among the different isolates of Trichoderma spp. evaluated, Trichoderma viride (AAU isolate) was highly antagonistic to F. oxysporum f. sp. ciceri (52.78%) and F. solani (65.37%) whereas, Trichoderma asperellum (AAU isolate) was highly antagonistic to M. phaseolina (65.93%). Panchagavya at the highest concentration (50%) showed significantly higher efficacy (80.74, 66.62 and 49.67%) in inhibiting the mycelial growth of all three pathogens and at the lowest concentration it was moderately effective.

Biocontrol Potential of Essential Oils in Organic Horticulture Systems: From Farm to Fork

In recent decades, increasing attention has been paid to food safety and organic horticulture. Thus, people are looking for natural products to manage plant diseases, pests, and weeds. Essential oils (EOs) or EO-based products are potentially promising candidates for biocontrol agents due to their safe, bioactive, biodegradable, ecologically, and economically viable properties. Born of necessity or commercial interest to satisfy market demand for natural products, this emerging technology is highly anticipated, but its application has been limited without the benefit of a thorough analysis of the scientific evidence on efficacy, scope, and mechanism of action. This review covers the uses of EOs as broad-spectrum biocontrol agents in both preharvest and postharvest systems. The known functions of EOs in suppressing fungi, bacteria, viruses, pests, and weeds are briefly summarized. Related results and possible modes of action from recent research are listed. The weaknesses of applying EOs are also discussed, such as high volatility and low stability, low water solubility, strong influence on organoleptic properties, and phytotoxic effects. Therefore, EO formulations and methods of incorporation to enhance the strengths and compensate for the shortages are outlined. This review also concludes with research directions needed to better understand and fully evaluate EOs and provides an outlook on the prospects for future applications of EOs in organic horticulture production.

A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques

Plant diseases cause major crop production losses worldwide, and a lot of significant research effort has been directed toward making plant disease identification and treatment procedures more effective. It would be of great benefit to farmers to be able to utilize the current technology in order to leverage the challenges facing agricultural production and hence improve crop production and operation profitability. In this work, we designed and implemented a user-friendly smartphone-based plant disease detection and treatment recommendation system using machine learning (ML) techniques. CNN was used for feature extraction while the ANN and KNN were used to classify the plant diseases; a content-based filtering recommendation algorithm was used to suggest relevant treatments for the detected plant diseases after classification. The result of the implementation shows that the system correctly detected and recommended treatment for plant diseases

AFD-Net: Apple Foliar Disease Multi Classification using Deep Learning on Plant Pathology Dataset

Plant diseases significantly affect the crop, so their identification is very important. Correct identification of these diseases is crucial for establishing a good disease control strategy to avoid time and financial losses. In general, machines can greatly reduce the possibility of human error. In particular, computer vision techniques developed through deep learning have paved a way to detect and diagnose these plant diseases on the leaf. In this work, the model AFD-Net was developed to detect and identify various leaf diseases in apple trees. The dataset is from Kaggle 2020 and 2021 and was financially supported by the Cornell Initiative for Digital Agriculture. A AFD-Net was proposed for leaf disease classification in apple trees and the results of the efficiency of the model are compared with other state-of-the-art deep learning approaches. The results of the experiments in the validation dataset show that the proposed AFD-Net model achieves the highest values compared to other deep learning models in the original and extended datasets with 98.7% accuracy for Plant Pathology 2020 and 92.6% for Plant Pathology 2021.

Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction

Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

Human vs. Machine, the Eyes Have It. Assessment of Stemphylium Leaf Blight on Onion Using Aerial Photographs from an NIR Camera

Aerial surveillance could be a useful tool for early detection and quantification of plant diseases, however, there are often confounding effects of other types of plant stress. Stemphylium leaf blight (SLB), caused by the fungus Stemphylium vesicarium, is a damaging foliar disease of onion. Studies were conducted to determine if near-infrared photographic images could be used to accurately assess SLB severity in onion research trials in the Holland Marsh in Ontario, Canada. The site was selected for its uniform soil and level topography. Aerial photographs were taken in 2015 and 2016 using an Xnite-Canon SX230NDVI with a near-infrared filter, mounted on a modified Cine Star—8 MK Heavy Lift RTF octocopter UAV. Images were taken at 15–20 m above the ground, providing an average of 0.5 cm/pixel and a field of view of 15 × 20 m. Photography and ground assessments of disease were carried out on the same day. NDVI (normalized difference vegetation index), green NDVI, chlorophyll index and plant senescence reflective index (PSRI) were calculated from the images. There were differences in SLB incidence and severity in the field plots and differences in the vegetative indices among the treatments, but there were no correlations between disease assessments and any of the indices.

Cyanobacteria: A Natural Source for Controlling Agricultural Plant Diseases Caused by Fungi and Oomycetes and Improving Plant Growth

Cyanobacteria, also called blue-green algae, are a group of prokaryotic microorganisms largely distributed in both terrestrial and aquatic environments. They produce a wide range of bioactive compounds that are mostly used in cosmetics, animal feed and human food, nutraceutical and pharmaceutical industries, and the production of biofuels. Nowadays, the research concerning the use of cyanobacteria in agriculture has pointed out their potential as biofertilizers and as a source of bioactive compounds, such as phycobiliproteins, for plant pathogen control and as inducers of plant systemic resistance. The use of alternative products in place of synthetic ones for plant disease control is also encouraged by European Directive 2009/128/EC. The present up-to-date review gives an overall view of the recent results on the use of cyanobacteria for both their bioprotective effect against fungal and oomycete phytopathogens and their plant biostimulant properties. We highlight the need for considering several factors for a proper and sustainable management of agricultural crops, ranging from the mechanisms by which cyanobacteria reduce plant diseases and modulate plant resistance to the enhancement of plant growth.