Crop Production Losses Research Articles

A spatial assessment of agricultural vulnerability to climate change using multidimensional data in Maharashtra state of India

This study aims to examine district-level agricultural vulnerability in the semi-arid and drought-prone state of Maharashtra. Using a multidimensional approach, suitable indicators for 34 districts were selected under exposure, sensitivity and adaptive capacity, aligning with the IPCC 2007 framework for vulnerability assessment. The analysis used temperature and rainfall data spanning 50 years (1968–2017). It also incorporated area and yield data from 2011 to 2020, along with other indicators for the year 2019–20. The composite agricultural vulnerability index (CAVI) score was obtained using proper normalization and assigning weights. The study revealed that the Marathwada and Vidarbha regions reported higher potential crop losses. Among the districts, Sangali was the most exposed (0.57), while Jalgaon was the least exposed (0.31) to climate variations. Ahmednagar exhibited the highest sensitivity (0.70), while Nagpur showed the lowest sensitivity (0.34) to climate change. Regarding adaptive capacity, Pune demonstrated the highest resilience (0.57) and Sindhudurg displayed the lowest score (0.23). CAVI highlighted that the Konkan region, along with western Maharashtra and eastern parts of Vidarbha, exhibited relatively lower vulnerability to climate change, due to factors such as lower potential crop loss and better adaptive capacity. In contrast, Marathwada and western Vidarbha regions were more vulnerable, facing significant crop production losses and higher exposure to climate variability. Hence, to build resilience to climate change and reduce crop production losses, crop planning needs to be adjusted according to the climatic conditions in these regions.

Impact of Arable Land Abandonment on Crop Production Losses in Ukraine During the Armed Conflict

The outbreak of Russia-Ukraine conflict casted an impact on the global food market, which was believed to be attributed to that Ukraine has suffered significant production losses due to cropland abandonment. Nevertheless, recent outbreaks of farmer protests against Ukraine’s grain exports demonstrated that the production losses might not be as severe as previous estimates. By utilizing the adaptive threshold segmentation method to extract abandoned cropland from the Sentinel-2 high-resolution imagery and calibrating the spatial production allocation model’s gridded crop production data from Ukraine’s statistical data, this study explicitly evaluated Ukraine’s crop-specific production losses and the spatial heterogeneity. The results demonstrated that the estimated area of abandoned cropland in Ukraine ranges from 2.34 to 2.40 million hectares, constituting 7.14% to 7.30% of the total cropland. In Ukrainian-controlled zones, this area spans 1.44 to 1.48 million hectares, whereas in Russian-occupied areas, it varies from 0.90 to 0.92 million hectares. Additionally, the total production losses for wheat, maize, barley, and sunflower amount to 1.92, 1.67, 0.70, and 0.99 million tons, respectively, with corresponding loss ratios of 9.10%, 7.48%, 9.54%, and 8.67%. Furthermore, production losses of wheat, barley, and sunflower emerged in both the eastern and southern states adjacent to the conflict frontlines, while maize losses were concentrated in the western states. The findings imply that Ukraine ought to streamline the food transportation channels and maintain stable agricultural activities in regions with high crop production.

Whitefly‐Pathogenic Beauveria bassiana JEF‐507 as a Competitive Biopesticide, Chongchae‐Stop

ABSTRACTSilverleaf whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae), is a destructive insect pest damaging to diverse crops by vectoring several plant pathogenic viruses, which consequently causes economic losses in crop production. As the resistance of whiteflies to chemical insecticides is increasing, this study aimed to investigate the potential of entomopathogenic fungi as an alternative. A total of 72 entomopathogenic fungal isolates, collected from soils using Tenebrio molitor larvae as an insect baiting method, were assessed for their virulence against 2nd nymphs of whitefly. Their virulence was assayed by dipping whitefly‐infested tomato leaves in fungal conidia suspensions at 1.0 × 107 conidia/mL. Among the tested isolates, two isolates of Beauveria bassiana JEF‐462 and JEF‐507 showed high virulence. In the assessment of virulence depending on conidia concentrations, the estimated LC50 values for JEF‐462 and JEF‐507 were similarly 8.7–14.0 × 107 conidia/mL. However, B. bassiana JEF‐507 showed higher conidial productivity and thermotolerance on most of tested 12 grain substrates than B. bassiana JEF‐462, and millet was the most suitable grain substrate. Additionally, siloxane as a surfactant was able to sufficiently exhibit the insecticidal activity of JEF‐507 against whitefly nymphs compared with other surfactants. In a pot‐based greenhouse trial, JEF‐507 showed higher control efficacy than chemical insecticides, dinotefuran and spinetoram. This work suggests that B. bassiana JEF‐507 could be competitively used as a biopesticide to control silverleaf whiteflies while overcoming current resistance issues. The JEF‐507 isolate has been registered in Korea, 2022 and successfully commercialised as the name of Chongchae‐Stop in this local market to control whitefly and thrips.

Potato Leaf Disease Detection Based on a Lightweight Deep Learning Model

Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by providing more accurate and efficient solutions. The management of potato diseases is critical to the agricultural industry, as these diseases can lead to substantial losses in crop production. The prompt identification and classification of potato leaf diseases are essential to mitigating such losses. In this paper, we present a novel approach that integrates a lightweight convolutional neural network architecture, RegNetY-400MF, with transfer learning techniques to accurately identify seven different types of potato leaf diseases. The proposed method not only enhances the precision of potato leaf disease detection but also reduces the computational and storage demands, with a mere 0.40 GFLOPs and a model size of 16.8 MB. This makes it well-suited for use on edge devices with limited resources, enabling real-time disease detection in agricultural environments. The experimental results demonstrated that the accuracy of the proposed method in identifying seven potato leaf diseases was 90.68%, providing a comprehensive solution for potato crop management.

Determination of the prevalence of fungi associated with amaranth cultivation in the locality of Kombé, Republic of Congo

Amaranth is a nutrient-rich leafy vegetable. Phytopathogenic fungi are causing major losses in amaranth crop production in the Kombé area. The detection of these phytopathogenic fungi can play an important role. The aim of this study is to assess the abundance of endophytic and saprophytic fungi associated with amaranth cultivation in Brazzaville, with a view to recommending biological control of fungal diseases. The prevalence of fungi associated with amaranth began with a survey, then continued in the laboratory. During the survey, 15 amaranth and soil samples were then collected separately in different stomacher bags, labelled and shipped to the laboratory where they were stored in a refrigerator at 4°C for four (4) weeks for analysis. In the laboratory, endophytic and saprophytic fungi were isolated using the fresh-state inoculation method for endophytic fungi and the suspension-dilution method for saprophytic fungi. The isolated strains were identified by determining their macroscopic and microscopic characteristics. The phytosanitary survey revealed that the most dominant symptom on amaranth plants in Kombé was wilting (42.86%). The results obtained from the distribution of endophyte isolates by colour indicate the presence of 5 isolates of unidentified endophytic fungi obtained from the leaf organ. Through isolation and microscopic analysis of soil saprophytes, six genera of fungi were identified: Botrytis cineara, Rhizoporus sp, Penicillium sp, Mucor sp, Scedospirium sp and Curvularia sp.

Innovative approach for assessing nitrogen loss risk to surface waters from crop production in a watershed scale through nitrogen surplus index method

Although various measures are applied to control farmland nitrogen loss at the field scale, effective control strategies at the watershed scale are still lacking due to poor understanding of nitrogen loss risks across different scales. Therefore, an innovative approach to assess the risk of nitrogen loss to surface waters from crop production at the watershed scale was established, with nitrogen surplus, a more accurate indicator, as a major source factor introduced to the traditional index method. Furthermore, this approach was applied in the Baiyangdian Lake Watershed, a typically agricultural-dominated watershed in China, and the control strategies for farmland nitrogen loss reduction were investigated with scenario analysis. The major results included: (1) Areas with high (2–4 for risk to river, 1.6–3.2 for risk to lake) and very high (>4 for risk to river, >3.2 for risk to lake) risks of nitrogen loss in crop production were relatively small. For example, in the wheat-maize rotation system, these areas accounted for only 2.1–3.5 % and 0.9–1.8% of the total study area, respectively. Additionally, the critical source areas of nitrogen loss were concentrated around the receiving water bodies. (2) The nitrogen surplus in wheat season increased the nitrogen loss to surface waters. Compared to the maize season, the area with high and very high risk for nitrogen loss to the nearby river in wheat-maize rotation system increased by 58.3% and 141.4%. Furthermore, the areas with high and very high risk for nitrogen loss to the lake in wheat-maize rotation system were 129.6% and 220.6% more than that during maize season. (3) Nitrogen loss risk to the nearby rivers was correlated with nitrogen surplus, but nitrogen loss risk to the lake was related to the transport coefficient. (4) The optimized scenario for nitrogen loss reduction in crop production was the combination of nitrogen surplus being less than 20 kg/ha and 500 m banning area around the river with no fertilizer application, which reduced the nitrogen loss risk to the nearby river and to the lake by 94–95% and 89–95%. The results of this study provide a scientific basis for nutrient resource management and non-point source pollution control.

A Cost–Benefit Analysis for the Economic Evaluation of Ecosystem Services Lost Due to Erosion in a Mediterranean River Basin

Soil degradation in Europe is mostly represented by soil erosion that, as a sediment production mechanism, is the main environmental threat to many watersheds, including the Bovilla watershed (Tirana), useful for the supply of drinking water to the city, and therefore, the care of water quality is of particular interest. The soil erosion of the Bovilla watershed was monitored in a work set up in June 2017. Following this work, this research updates the previous data on soil loss at the Bovilla watershed in t/ha/year to September 2019 and focuses on the identification and monetary evaluation of the ecosystem services (ESs) linked to soil erosion (loss of carbon, loss of mineral elements, habitat quality, crop productivity, and sustainable tourism suitability). Then, we applied the replacement cost analysis to test the economic convenience and suggest the adoption of sustainable land management practices (e.g., reforestation) able to improve the quality water in this watershed. The study carried out demonstrates that the values of soil lost due to erosion vary depending on the type of land use (ranging from average values of 120.32 t/ha for bare land to values of 8.16 t/ha for wooded areas). Furthermore, from the application of monetary methods for the evaluation of some ecosystem services linked to erosion (loss of carbonaceous and mineral elements, habitat quality, productivity, suitability for sustainable tourism), it clearly emerges that the value of the productivity of agricultural crops varies from EUR 0 to 35,320.50/ha and that the service represents a more incisive service than the previous ones, so much so as to make the conversion of some agricultural land with high productivity values into wooded areas economically disadvantageous. The data from this study may help to develop Bovilla watershed management strategies for erosion and pollution control and sediment remediation mainly in agricultural lands. A program of measures can be effective for controlling soil erosion, but it must be implemented over long time frames, and it requires relevant investments from the public and private sectors, also with a view to increase the allocation of economic values of monetary compensation aimed at those who decide to start forestation projects on highly productive soils.

Unraveling the complex interactions between ozone pollution and agricultural productivity in China's main winter wheat region using an interpretable machine learning framework

Surface ozone has become a significant atmospheric pollutant in China, exerting a profound impact on crop production and posing a serious threat to food security. Previous studies have extensively explored the physiological mechanisms of ozone damage to plants. However, the effects of ozone interactions with other environmental factors, such as climate change, on agricultural productivity at the regional scale, particularly under natural conditions, remain insufficiently understood. In this study, we employed an interpretable machine learning framework, specifically the eXtreme Gradient Boosting (XGBoost) algorithm enhanced by SHapley Additive exPlanations (SHAP), to investigate the influence of ozone and its interactions with environmental factors on crop production in China's primary winter wheat region. Additionally, a structural equation model was developed to elucidate the mechanisms driving these interactions. Our findings demonstrate that ozone pollution exerts a significant negative effect on winter wheat productivity (r = −0.47, P < 0.001), with productivity losses escalating from −12.28 % to −22.09 % as ozone levels increase. Notably, the impact of ozone is spatially heterogeneous, with western Shandong province identified as a hotspot for ozone-induced damage. Furthermore, our results confirm the complexity of the relationship between ozone pollution and agricultural productivity, which is influenced by multiple interacting environmental factors. Specifically, we found that severe ozone pollution, when combined with high aerosol concentrations or elevated temperatures, significantly exacerbates crop productivity losses, although drought conditions can partially mitigate these adverse effects. Our study highlights the importance of incorporating the interactive effects of air pollution and climate change into future crop models. The comprehensive framework developed in this study, which integrates statistical modeling with explainable machine learning, provides a valuable methodological reference for quantitatively assessing the impact of air pollution on crop productivity at a regional scale.

Influence of silicon application on the growth of corn under conditions of water stress

The low availability of water in the soil is one of the precursors of stress in the plant, responsible for the decrease in growth and losses in crop productivity, by hindering the maintenance of essential metabolic processes. Many experimental results have demonstrated the effect of silicon (Si) as an attenuator of environmental stresses in plants. Thus, the objective of this research was to evaluate the growth of the corn crop subjected to water stress and the application of silicon in the semi-arid region of Alagoas. The experimental design used was in strips with four replications, schematized in subdivided plots. The culture was subjected to a combination of five water stress conditions (1, 4, 7, 10 and 13 days of intervals of irrigation) and two conditions of availability of Si (with and without Si). The following plant variables were analyzed: canopy height, stem diameter and leaf area index. It was found that the longer the irrigation interval, the lower the growth of plants. The application of Si provided higher rates of leaf area. The use of Si in plant fertilization in water deficit conditions is a viable alternative.

Archetypes of climate change adaptation among large-scale arable farmers in southern Romania

Effects of climate change and especially the associated climate variability require farmers to adjust to increasing frequencies of extreme events. In the agriculturally highly productive Romanian Plain, the frequency, intensity, and duration of heatwaves and drought have increased over the past 20 years. Although recent surveys revealed farmers’ awareness of climate change and enumerated a number of farm adaptation measures in the Romanian context, a systems approach to adaptation that allows conclusions on farm vulnerability and adaptive capacity is missing. Here, we use archetypal analysis to elucidate and characterize for the first time the types of adaptation responses of arable farmers in southern Romania. We conducted semi-structured interviews with 30 farmers managing 51,500 ha located across the southern lowlands of Romania, selected for their diversity of management approaches. Farmers were asked about experienced climatic disturbances, crop production losses during the most extreme events over the past 5–10 years, and the adaptation measures they implemented over that period of time. In addition, structural characteristics of the farm were recorded. The adaptation measures were classified and mapped on the efficiency, substitution, and redesign gradient used to classify sustainability stages. Results revealed three archetypes of adaptation, consisting of measures at field and farm level ranging from predominantly efficiency-enhancing ones (e.g., crop choice and management and risk insurance) to complete farm redesign involving agrotechnical and financial management changes. Structural farm characteristics did not explain differences between farms in their association with one of the archetypes. Our approach and results show for the first time both the need for strengthening farmer-level support in one of Europe’s key food production areas and the lessons that can be drawn from the outlier adaptation examples. Current European and national policies offer opportunities for farmer organizations in Romania to make these conclusions actionable.

Occurrence and management of the emerging pathogen Epicoccum sorghinum.

Epicoccum sorghinum is a notorious fungal pathogen that causes leaf spot symptoms on a wide range of plants, leading to devastating losses in crop production and quality. Here, all reports regarding the occurrence and management of E. sorghinum are covered for the first time. E. sorghinum has been detected in tropical and subtropical climate areas during the rainy season, mainly from March to August, since 2016. Although E. sorghinum shows broad host spectrum, the disease incidence is especially notorious in cereal crops and ornamental plants, suggesting that these plants are especially susceptible. Control methods based on synthetic fungicides, plant extracts, and microbial biocontrol agents have been reported. However, most agents were applied using only in vitro conditions, restricting the information about their actual applicability in field conditions. Additionally, E. sorghinum can colonize cereal grains and synthesize the carcinogenic mycotoxin tenuazonic acid, posing an enormous hazard for human health. Furthermore, although E. sorghinum is an emerging pathogen that is currently causing yield penalties in important crops, there is lack of information about its pathogenic mechanisms and virulence factors, and there is currently no commercial antifungal agent to manage E. sorghinum. Collectively, it is imperative to conduct in vivo studies to determine the efficacy of antifungal agents and the most effective methods of application in order to develop suitable management strategies against E. sorghinum.

Optimizing molecular assays for glyphosate and ALS-inhibitor resistance diagnostics in four weed species

Herbicide-resistant weeds pose a threat to food production in modern agriculture, causing US$32 billion in crop production losses worldwide. In Michigan, highly troublesome and widespread weeds include waterhemp, Palmer amaranth, common ragweed, and horseweed, with accessions that are resistant to glyphosate (Group 9) and ALS-inhibitors (Group 2), major herbicide sites of action utilized in soybean and corn cropping systems. Molecular assays for rapid resistance diagnostics to confirm the in-field status of herbicide resistance can assist with more effective, timely, and proactive management. In this research, we developed and tested PCR-based assays to identify target site resistance mechanisms to both herbicide groups through Sanger sequencing and EPSPS copy number variation. Nine different SNPs were identified in five ALS positions known to confer herbicide resistance among all species surveyed. Pro197Ser was the most frequent in horseweed and common ragweed accessions, whereas Trp574Leu was the predominant mutation in Palmer amaranth and waterhemp. Four horseweed accessions contained the Pro106Ser mutation in the EPSPS gene, which confers resistance to glyphosate. Additionally, waterhemp and Palmer amaranth had 2–7 and 20–160 copies of EPSPS, respectively. The assays were validated by comparing genotyping of several field-collected accessions of unknown resistance status with known resistant and susceptible accessions. The efficacy of genotyping assays was &gt;98% and required only two days, confirming that molecular assays are a robust tool for rapid resistance diagnostics. These assays can help growers evaluate herbicide resistance status in weed populations within the same growing season, allowing them to adopt effective management practices.

Deciphering MAP kinase pathway in Cicer arietinum upon Helicoverpa armigera -infestation

The activation of Mitogen-Activated Protein Kinase (MAPK) pathway is recognized as a key regulator of the plant defense mechanisms in response to insect attacks. The MAPK pathway comprises three main components: MAP Kinase Kinase Kinase (MAPKKK), MAP Kinase Kinase (MAPKK) and MAP Kinase (MAPK). The MAPKKK is the first kinase in the MAPK cascade, which typically responds to external stimuli and activates downstream component, MAPKK, which, in turn, activates the final component, MAPK, which further plays a key role in regulating gene expression of defense-associated transcription factors. Elucidation of complete MAPK pathway and its components has been investigated in the model plants in the past, however, it has not been attempted in leguminous plant, chickpea, which is attacked vigorously by Helicoverpa armigera, causing huge loss in crop production. Therefore, the study aims to decipher and elucidate MAP kinase pathway activated during chickpea-H. armigera interaction. MAPKKK, MAPKK, MAPK are a family of genes and specific genes from each components, were identified and selected for further analysis based on their expression patterns in response to H. armigera-infestation. Further, three dimensional structure prediction, validation, docking and simulation of the selected proteins from each component of the pathway, were executed and specific components from chickpea were predicted. The probable pathway forecasted is MEKK4/Raf22 (MAPKKK)-MAPKK(4)10-MAPK12/6). This article provides an overview of the molecular mechanisms involved in plant's response to pest attacks, unravelling the components of MAPK pathway in chickpea induced during herbivory. Further validation of this pathway by in vitro/in vivo approaches is highly recommended.

A new biostimulant derived from soybean by-products enhances plant tolerance to abiotic stress triggered by ozone

BackgroundTropospheric ozone is an air pollutant that causes negative effects on vegetation, leading to significant losses in crop productivity. It is generated by chemical reactions in the presence of sunlight between primary pollutants resulting from human activity, such as nitrogen oxides and volatile organic compounds. Due to the constantly increasing emission of ozone precursors, together with the influence of a warming climate on ozone levels, crop losses may be aggravated in the future. Therefore, the search for solutions to mitigate these losses becomes a priority.Ozone-induced abiotic stress is mainly due to reactive oxygen species generated by the spontaneous decomposition of ozone once it reaches the apoplast. In this regard, compounds with antioxidant activity offer a viable option to alleviate ozone-induced damage. Using enzymatic technology, we have developed a process that enables the production of an extract with biostimulant properties from okara, an industrial soybean byproduct. The biostimulant, named as OEE (Okara Enzymatic Extract), is water-soluble and is enriched in bioactive compounds present in okara, such as isoflavones. Additionally, it contains a significant fraction of protein hydrolysates contributing to its functional effect.Given its antioxidant capacity, we aimed to investigate whether OEE could alleviate ozone-induced damage in plants. For that, pepper plants (Capsicum annuum) exposed to ozone were treated with a foliar application of OEE.ResultsOEE mitigated ozone-induced damage, as evidenced by the net photosynthetic rate, electron transport rate, effective quantum yield of PSII, and delayed fluorescence. This protection was confirmed by the level of expression of genes associated with photosystem II. The beneficial effect was primarily due to its antioxidant activity, as evidenced by the lipid peroxidation rate measured through malondialdehyde content. Additionally, OEE triggered a mild oxidative response, indicated by increased activities of antioxidant enzymes in leaves (catalase, superoxide dismutase, and guaiacol peroxidase) and the oxidative stress index, providing further protection against ozone-induced stress.ConclusionsThe present results support that OEE protects plants from ozone exposure. Taking into consideration that the promotion of plant resistance against abiotic damage is an important goal of biostimulants, we assume that its use as a new biostimulant could be considered.

Biotechnological Approaches toward Blast Disease Resistance in Finger Millet

Plant disease globally causes significant losses in crop production that hindering the urgent need for a 60% increase in food demand. The main impact is reduced food quantity and quality leading to compromised food safety from pesticides and toxins. Biotechnology is essential for protecting crop yield and enhancing sustainability in agriculture. Agricultural biotechnology has advanced offering options for understanding plant molecular mechanisms and breeding. The devastating finger millet blast disease caused by the fungus Magnaporthe oryzae affects crop growth and yield leading to instability due to breakdown of blast resistance. Novel strategies are essential for sustainable finger millet production focusing on breeding techniques such as molecular markers, expressed sequence tags (ESTs), gene expression profiling, genome-wide association studies, and genetic transformations to develop new disease-resistance varieties. This comprehensive review explores current breeding strategies and future directions that may offers insights for effective disease control and optimizing finger millet productivity.

Agrochemicals and crop productivity losses

Objective: The aim of this research is to reflect briefly on the importance of the use of agrochemicals in the productivity of some crops according to their contribution to yield, and some implications of their agricultural consumption. Design/methodology/approach: Based on related literature and some experiences in the field on the use and consumption of pesticides. Results: Pesticides are synthetic, microbial or derived organic compounds used in plant growth programs to prevent or control pests, diseases, and weeds. Also, mineral nutrition (with macro and micronutrients) is considered as agrochemicals. However, the effects on the contamination of soils, groundwater, lakes, seas and oceans due to its use is increasing. Mexico is a large consumer of fungicides, insecticides, fertilizers and herbicides for agricultural uses. Limitations on study/implications: Unmeasured use of any type of pesticide can produce tons of pesticide-trash. Some traces of active or inert ingredients can be detected in bodies of water. Findings/conclusions: The constant risk of agronomic yield losses can be substantial without the use of agrochemicals. Without the application of pesticides, yield losses can reach 100%.

Impact of ozone pollution on crop yield, human health, and associated economic costs in the Indo-Gangetic plains

Ozone pollution is a growing problem in many developing countries posing challenges not only to air quality but also affecting agricultural productivity and human well-being. This is the first study in the Indo-Gangetic Plain exploring how the spatial variation and severity of tropospheric ozone affect both wheat yield and all-cause mortality. We estimated that ozone-related cumulative crop production loss for wheat in selected districts of IGP was 3.4 million tonnes during the study period (2019–2021), which amounted to 923 million USD. The production-weighted Relative Yield Loss (RYL) for wheat in the IGP was 9.3 % in 2019, 12.8 % in 2020, and 11.3 % in 2021. The losses incurred in 2021 could contribute to fulfilling the wheat requirements of 11.4 million people. We also assess the health and economic gains resulting from the attainment of the World Health Organization Air Quality Guidelines (WHO AQG) for ozone concentrations. It is estimated that interventions that achieve AQG would have averted 11,407 premature deaths in 2021 translating into an impressively large health and economic gain. The annual benefits in 2021 totaled to 34 billion USD. We observe that Uttar Pradesh experienced the highest losses, both in terms of crop damage and premature deaths. Our study observes that implementing policies to prepone the planting of wheat enhances food security by mitigating yield losses. Mitigating the health impact of ambient ozone necessitates a reduction in anthropogenic emissions and to attain this objective, we propose adopting an exposure-integrated source reduction approach.

A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Convolutional Neural Network

Plant diseases pose a significant challenge to agriculture in India, causing substantial losses in crop production each year. Limited-resource farmers often struggle with diagnosing these diseases accurately, especially when relying on visual observation of leaf symptoms. Hence, there is an urgent need to enhance the detection, monitoring, and prediction of crop diseases to mitigate agricultural losses effectively. To address this challenge, we propose a mobile-based system empowered by Machine Learning (ML) and computer vision tailored for the Indian context. Our system utilizes Convolutional Neural Networks (CNNs) as the core deep learning engine to classify 37 disease categories commonly found in Indian crops. We have curated a comprehensive dataset comprising 96,206 images of plant leaves, encompassing both healthy and infected, for training, validation, and testing purposes. The user interface of our system is designed as an Android mobile application, enabling farmers to capture images of diseased plant leaves effortlessly. Upon image capture, the system swiftly identifies the disease category and presents it to the user along with a confidence percentage. This functionality empowers farmers to take timely action to protect their crops, thereby reducing reliance on incorrect fertilizers that may exacerbate plant stress. Furthermore, we evaluated the performance of our system using various metrics, including classification accuracy and processing time. Our results indicate an impressive overall classification accuracy of 94% across the 37 disease classes prevalent in 14 different crop species commonly cultivated in India. In essence, our ML-powered mobile-based solution offers Indian farmers a robust tool for efficient crop disease diagnosis and management, ultimately contributing to the enhancement of agricultural productivity and sustainability in the country.

Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts' total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards.

Application of inhibitors targeting the type III secretion system in phytopathogenic bacteria

Plant bacterial diseases have inflicted substantial economic losses in global crop, fruit, and vegetable production. The conventional methods for managing these diseases typically rely on the application of antibiotics. However, these antibiotics often target the growth factors of the pathogenic bacteria, leading to the accumulation and emergence of drug-resistant strains, which exacerbates antibiotic resistance. Innovative methods are urgently needed to treat and prevent the toxicity caused by these pathogenic bacteria. Targeting virulence mechanisms in pathogens is a globally recognized and effective strategy for mitigating bacterial resistance. Type III secretion system (T3SS) serves as a crucial virulence determinant in Gram-negative pathogens, and its non-essentials for pathogen growth renders it an ideal target. Targeting the T3SS holds significant potential to alleviate selective pressure for resistance mutations in pathogens. Therefore, targeting T3SS in pathogenic bacteria, while preserving their growth, has emerged as a novel avenue for the development of antimicrobial drugs. In recent years, a multitude of small molecular inhibitors targeting T3SS have been identified. This article offers a comprehensive review of T3SS inhibitors in plant pathogens, while also presenting the latest research advancements in this research direction.