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Book part
Publication date: 15 July 2017

Andreas W. Ebert

Malnutrition is widespread and affects about one-third of humanity. Increasing production and consumption of vegetables is an obvious pathway to improve dietary diversity…

Abstract

Malnutrition is widespread and affects about one-third of humanity. Increasing production and consumption of vegetables is an obvious pathway to improve dietary diversity, nutrition and health. This chapter analyses how climate change is affecting vegetable production, with a special focus on the spread of insect pests and diseases. A thorough literature review was undertaken to assess current global vegetable production, the factors that affect the spread of diseases and insect pests, the implications caused by climate change, and how some of these constraints can be overcome. This study found that climate change combined with globalization, increased human mobility, and pathogen and vector evolution has increased the spread of invasive plant pathogens and other species with high fertility and dispersal. The ability to transfer genes from wild relatives into cultivated elite varieties accelerates the development of novel vegetable varieties. World Vegetable Center breeders have embarked on breeding for multiple disease resistance against a few important pathogens of global relevance and with large evolutionary potential, such as chili anthracnose and tomato bacterial wilt. The practical implications of this are that agronomic practices that enhance microbial diversity may suppress emerging plant pathogens through biological control. Grafting can effectively control soil-borne diseases and overcome abiotic stress. Biopesticides and natural enemies either alone or in combination can play a significant role in sustainable pathogen and insect pest management in vegetable production system. This chapter highlights the importance of integrated disease and pest management and the use of diverse production systems for enhanced resilience and sustainability of highly vulnerable, uniform cropping systems.

Article
Publication date: 19 November 2021

Swathi Kailasam, Sampath Dakshina Murthy Achanta, P. Rama Koteswara Rao, Ramesh Vatambeti and Saikumar Kayam

In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy…

Abstract

Purpose

In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.

Design/methodology/approach

In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.

Findings

In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.

Originality/value

The implemented machine learning design is outperformance methodology, and they are proving good application detection rate.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Open Access
Article
Publication date: 18 May 2018

Winifred Chepkoech, Nancy W. Mungai, Silke Stöber, Hillary K. Bett and Hermann Lotze-Campen

Understanding farmers’ perceptions of how the climate is changing is vital to anticipating its impacts. Farmers are known to take appropriate steps to adapt only when they…

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Abstract

Purpose

Understanding farmers’ perceptions of how the climate is changing is vital to anticipating its impacts. Farmers are known to take appropriate steps to adapt only when they perceive change to be taking place. This study aims to analyse how African indigenous vegetable (AIV) farmers perceive climate change in three different agro-climatic zones (ACZs) in Kenya, identify the main differences in historical seasonal and annual rainfall and temperature trends between the zones, discuss differences in farmers’ perceptions and historical trends and analyse the impact of these perceived changes and trends on yields, weeds, pests and disease infestation of AIVs.

Design/methodology/approach

Data collection was undertaken in focus group discussions (FGD) (N = 211) and during interviews with individual farmers (N = 269). The Mann–Kendall test and regression were applied for trend analysis of time series data (1980-2014). Analysis of variance and least significant difference were used to test for differences in mean rainfall data, while a chi-square test examined the association between farmer perceptions and ACZs. Coefficient of variation expressed as a percentage was used to show variability in mean annual and seasonal rainfall between the zones.

Findings

Farmers perceived that higher temperatures, decreased rainfall, late onset and early retreat of rain, erratic rainfall patterns and frequent dry spells were increasing the incidences of droughts and floods. The chi-square results showed a significant relationship between some of these perceptions and ACZs. Meteorological data provided some evidence to support farmers’ perceptions of changing rainfall. No trend was detected in mean annual rainfall, but a significant increase was recorded in the semi-humid zone. A decreasing maximum temperature was noted in the semi-humid zone, but otherwise, an overall increase was detected. There were highly significant differences in mean annual rainfall between the zones. Farmers perceived reduced yields and changes in pest infestation and diseases in some AIVs to be prevalent in the dry season. This study’s findings provide a basis for local and timely institutional changes, which could certainly help in reducing the adverse effects of climate change.

Originality/value

This is an original research paper and the historical trends, farmers’ perceptions and effects of climate change on AIV production documented in this paper may also be representative of other ACZs in Kenya.

Details

International Journal of Climate Change Strategies and Management, vol. 10 no. 4
Type: Research Article
ISSN: 1756-8692

Keywords

Book part
Publication date: 28 March 2022

Altaf Alam, Anurag Chauhan, Mohd Tauseef Khan and Zainul Abdin Jaffery

In this chapter, drone and vision camera technology have been combined for monitoring the crop product quality. Three vegetable crops such as tomato, cauliflower, and…

Abstract

In this chapter, drone and vision camera technology have been combined for monitoring the crop product quality. Three vegetable crops such as tomato, cauliflower, and eggplant are considered for quality monitoring; hence, image datasets are collected for those vegetables only. The proposed method classified the vegetables into two classes as rotten and nonrotten products so the images were collected for rotten and nonrotten products. Three different features information such as chromatic features, contour features, and texture features have been extracted from the dataset and further used to train a Gaussian kernel support vector machine algorithm for identifying the product quality. The system utilized multiple features such as chromatic, contour, and texture features in classifier training which enhances the accuracy and robustness of the system. Chromatic features were utilized for detecting the crop while other features such as contour and texture features were utilized for further classifier building to identify the crop product quality. The performance of the system is evaluated based on the true positive rate, false discovery rate, positive predictive value, and accuracy. The proposed system identified good and bad products with a 97.9% of true positive rate, 2.43 % of false discovery rate, 97.73% positive predictive value, and 95.4% of accuracy. The achieved results concluded that the results are lucrative and the proposed system is efficient in agriculture product quality monitoring.

Article
Publication date: 21 March 2016

Wondimagegn Tesfaye and Lemma Seifu

The purpose of this paper is to analyze smallholder farmers’ perceptions of climate change and its adverse effects, identify major adaptation strategies used by farmers…

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Abstract

Purpose

The purpose of this paper is to analyze smallholder farmers’ perceptions of climate change and its adverse effects, identify major adaptation strategies used by farmers and analyze the factors that influence the choice of adaptation strategy by smallholder farmers in eastern Ethiopia.

Design/methodology/approach

The study was based on a cross-sectional survey of 296 sample households selected from three districts in east Ethiopia. Data were collected with the aid of a semi-structured questionnaire and review of literature, documents and databases.

Findings

The study provides empirical evidence that majority of farmers in the study area are aware of climate change patterns and their adverse effect on income, food security, diversity, forest resources, food prices and crop and livestock diseases. In response to these adverse effects, major adaptation strategies used by farmers include cultivating different crops, planting different crop varieties, changing planting dates, use of soil and water conservation techniques, conservation agriculture practices and engaging in non-farm income activities. Choice of adaptation strategies are influenced by gender of household head, household size, farm size, distance from market and number of farm plots.

Practical implications

The study suggests that developing more effective climate change adaptation strategies need support from the government. Such an effort needs provision of the necessary resources such as credit, information and extension services on climate change adaptation strategies and technologies, and investing in climate smart and resilient projects.

Originality/value

The study adopts multivariate probit model that models farmers’ simultaneous adaptation choice behavior which has been rarely addressed by previous researches.

Details

International Journal of Climate Change Strategies and Management, vol. 8 no. 2
Type: Research Article
ISSN: 1756-8692

Keywords

Article
Publication date: 22 July 2021

Rob Bogue

This paper aims to illustrate the growing importance of agricultural robots by providing details of recent product developments and their applications.

Abstract

Purpose

This paper aims to illustrate the growing importance of agricultural robots by providing details of recent product developments and their applications.

Design/methodology/approach

Following a short introduction, this first discusses a range of agricultural applications of drones. It then provides details of a selection of mobile field robots and their applications. Finally, concluding comments are drawn.

Findings

Commercially available aerial and terrestrial robots are playing a rapidly growing role in a diversity of agricultural practices. Key capabilities and benefits include detecting crop stress and disease, predicting crop yields, reducing agrochemical use, overcoming manpower shortages and reducing labour costs and facilitating precision agricultural practices such as highly localised pesticide and herbicide application and the replacement of large, heavy agricultural machines by fleets of small, lightweight robots.

Originality/value

This provides a detailed insight into the many ways in which robots are transforming agricultural practices.

Details

Industrial Robot: the international journal of robotics research and application, vol. 48 no. 5
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 22 December 2021

C. Ganeshkumar, Sanjay Kumar Jena, A. Sivakumar and T. Nambirajan

This paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and…

Abstract

Purpose

This paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.

Design/methodology/approach

The authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.

Findings

Fifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.

Research limitations/implications

The authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.

Originality/value

Earlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

Details

Journal of Agribusiness in Developing and Emerging Economies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-0839

Keywords

Article
Publication date: 1 June 2004

N.A. Amusa, I.A. Kehinde and A.A. Adegbite

The etiology of fruit anthracnose in hot pepper (Capsicum frutescens) was investigated at Ibadan, Osogbo, and Ikenne in the lowland forest zone of western Nigeria…

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Abstract

The etiology of fruit anthracnose in hot pepper (Capsicum frutescens) was investigated at Ibadan, Osogbo, and Ikenne in the lowland forest zone of western Nigeria. Collectotrichum capsici (Synd) Butler & Bisby was found associated with the fruit anthracnose of hot pepper in all locations. Out of 300 plants examined in all the locations, over 70 per cent had fruit anthracnose, while in some pepper fields all the fruits produced had the disease symptom. The pathogen overseasoned in pepper plant debris. A high inoculum population of 4.9×106 g−1 colony forming units/g was estimated in the soil of pepper fields. The seed from the infected hot pepper fruits also carried propergules of the pathogen. The fungus was also found on Lycopersicon esculentus, C. annum and Vigna unguiculata growing in and around the pepper fields. Pepper fruits infection by the disease occurs during the peak of the rainy season beginning in patches which spread later, resulting in extensive infection of the pepper field.

Details

Nutrition & Food Science, vol. 34 no. 3
Type: Research Article
ISSN: 0034-6659

Keywords

Article
Publication date: 1 September 2004

Colin Green

From a systems perspective, vulnerability can be defined as the relationship between a purposive system and its environment, where that environment varies over time. Which…

3053

Abstract

From a systems perspective, vulnerability can be defined as the relationship between a purposive system and its environment, where that environment varies over time. Which environmental perturbations are significant therefore depends upon the objectives of the system as only those perturbations that can inhibit the achievement of these objectives are significant. That system must decide whether to adjust in advance to each potential perturbation or to rely upon a recovery path when that perturbation occurs. In each case, it must then decide upon the adjustment or recovery path to adopt. In particular, the basic resources available to a household are time and energy where the rates at which these can be directly or indirectly, through earning income, converted to consumption are crucial. Perturbations can reduce the energy available as well as reduce the efficiencies with which time and energy can be converted to income.

Details

Disaster Prevention and Management: An International Journal, vol. 13 no. 4
Type: Research Article
ISSN: 0965-3562

Keywords

Article
Publication date: 4 December 2017

Aurélie Brunie, Diana Rutherford, Emily B. Keyes and Samuel Field

The purpose of this paper is to examine the impact of savings and loan groups (SGs), alone and combined with a rotating labor scheme (Ajuda Mútua), on the economic…

Abstract

Purpose

The purpose of this paper is to examine the impact of savings and loan groups (SGs), alone and combined with a rotating labor scheme (Ajuda Mútua), on the economic conditions of the rural poor in Nampula province in Mozambique.

Design/methodology/approach

Three pairs of districts were randomized into receiving SG, SG and AM, or no intervention. The study used a mixed-methods sequential explanatory design. Data from a longitudinal survey of 1,276 households were analyzed using difference-in-difference estimation to assess the impact of SGs on income and asset ownership. Thematic analysis of in-depth interviews with 72 program participants explored specific contributions of SGs to economic outcomes.

Findings

Survey results show that program participation had a significant, positive impact on income and asset ownership. Qualitative results indicate that SGs allowed households to bridge seasonal food consumption gaps and meet cash needs during crises. Accumulated savings supported asset purchases. Program activities supported agricultural activity, but enterprise development had limited scope. Challenges to economic development included cultural aversion to risk, inadequate agricultural inputs, low market integration, and limited business opportunities.

Practical implications

SGs helped reduce vulnerability to stress events. Programs should analyze the wider structural context to foster a positive enabling environment, and combine SGs with relevant enterprise development services for additional benefits.

Originality/value

The importance of savings is increasingly acknowledged, but the contributions and limitations of SGs are not fully understood. This paper also highlights the role of structural context, which remains undervalued in the literature.

Details

International Journal of Social Economics, vol. 44 no. 12
Type: Research Article
ISSN: 0306-8293

Keywords

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