Smart agricultural technology 

About: Smart agricultural technology is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Computer science & Biology. It has an ISSN identifier of 2772-3755. It is also open access. Over the lifetime, 194 publications have been published receiving 486 citations.

The digitization of agricultural industry – a systematic literature review on agriculture 4.0

Abstract: • SLR is conducted using PRISMA approach and148 articles are selected and critically analyzed. • The results show the extent of digital technologies adoption in agriculture. • The potential benefits of digital technologies and roadblocks hindering their implementation in agriculture sector are identified and discussed. • The study will positively impact the research around agriculture 4.0. Agriculture is considered one of the most important sectors that play a strategic role in ensuring food security. However, with the increasing world's population, agri-food demands are growing — posing the need to switch from traditional agricultural methods to smart agriculture practices, also known as agriculture 4.0. To fully benefit from the potential of agriculture 4.0, it is significant to understand and address the problems and challenges associated with it. This study, therefore, aims to contribute to the development of agriculture 4.0 by investigating the emerging trends of digital technologies in the agricultural industry. For this purpose, a systematic literature review based on Protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses is conducted to analyse the scientific literature related to crop farming published in the last decade. After applying the protocol, 148 papers were selected and the extent of digital technologies adoption in agriculture was examined in the context of service type, technology readiness level, and farm type. The results have shown that digital technologies such as autonomous robotic systems, internet of things, and machine learning are significantly explored and open-air farms are frequently considered in research studies (69%), contrary to indoor farms (31%). Moreover, it is observed that most use cases are still in the prototypical phase. Finally, potential roadblocks to the digitization of the agriculture sector were identified and classified at technical and socio-economic levels. This comprehensive review results in providing useful information on the current status of digital technologies in agriculture along with prospective future opportunities.

A Survey on Using Deep Learning Techniques for Plant Disease Diagnosis and Recommendations for Development of Appropriate Tools

Abstract: Several factors associated with disease diagnosis in plants using deep learning techniques must be considered to develop a robust system for accurate disease management. A considerable number of studies have investigated the potential of deep learning techniques for precision agriculture in the last decade. However, despite the range of applications, several gaps within plant disease research are yet to be addressed to support disease management on farms. Thus, there is a need to establish a knowledge base of existing applications and identify the challenges and opportunities to help advance the development of tools that address farmers' needs. This study presents a comprehensive overview of 70 studies on deep learning applications and the trends associated with their use for disease diagnosis and management in agriculture. The studies were sourced from four indexing services, namely Scopus, IEEE Xplore, Science Direct, and Google Scholar, and 11 main keywords used were Plant Diseases, Precision Agriculture, Unmanned Aerial System (UAS), Imagery Datasets, Image Processing, Machine Learning, Deep Learning, Transfer Learning, Image Classification, Object Detection, and Semantic Segmentation. The review is focused on providing a detailed assessment and considerations for developing deep learning-based tools for plant disease diagnosis in the form of seven key questions pertaining to (i) dataset requirements, availability, and usability, (ii) imaging sensors and data collection platforms, (iii) deep learning techniques, (iv) generalization of deep learning models, (v) disease severity estimation, (vi) deep learning and human accuracy comparison, and (vii) open research topics. These questions can help address existing research gaps by guiding further development and application of tools to support plant disease diagnosis and provide disease management support to farmers.

Unlocking adoption challenges of IoT in Indian Agricultural and Food Supply Chain

Abstract: • The factors affecting the adoption of IoT in the agri-food domain have been identified. • A MCDM approach has been used for analysing the factors. • The cause-effect relationships amongst the factors were identified. • The most critical factors have been recognised. • This study guides the stake holders to formulate new strategies/policies The Internet of Things (IoT) can play a key role in transforming traditional agricultural sector to smart agricultural domain. However, in developing economies the adoption is in the nascent stage. This paper aims to evaluate the critical factors that impact the adoption decision of IoT technology in the Agricultural and Food Supply Chain (AFSC). Based on a comprehensive literature survey and experts’ opinion 24 critical factors were identified. The identified list of factors was categorized into technological, social, economic, and organizational categories. DEMATEL method was applied to determine the cause-effect relationship of these factors. The results underlined five significant causal factors namely lack of interoperability, environmental sustainability, trust, lack of security, and network challenges which influence the IoT adoption. The results provide unique insights in agro-food sector to improve performance by overcoming the identified key challenges. Also, this study provides a roadmap for the implementation of IoT in emerging economies. Further, this paper guides the agri-food managers, IoT service providers, and the Government to formulate new strategies/policies for the effective adoption of IoT in the agri-food sector

Crops yield prediction based on machine learning models: Case of West African countries

Abstract: Global agricultural production, in particular, is of increasing concern to the major international organizations in charge of nutrition. The rising demand for food globally due to unprecedented population growth has led to food insecurity in some populated regions such as Africa. Another contributing factor to global food insecurity is climate change and its variability. World and African agricultural production in particular are of increasing concern to the major international organizations in charge of nutrition. The World Food Program has reported that high population growth worldwide, especially in Africa in recent years, is leading to increased food security. Moreover, farmers and agricultural decision-makers need advanced tools to help them make quick decisions that will impact the quality of agricultural yields. Climate change has been a major phenomenon in recent decades all over the world. An impact of climate change has been observed on the quality of agricultural production. The arrival of big data technology has led to new powerful analytical tools like machine learning, which have proven themselves in many areas such as medicine, finance, and biology. In this work, we propose a prediction system based on machine learning to predict the yield of six crops, namely: rice, maize, cassava, seed cotton, yams, and bananas, at the country-level in the area of West African countries throughout the year. We combined climatic data, weather data, agricultural yields, and chemical data to help decision-makers and farmers predict the annual crop yields in their country. We used a decision tree, multivariate logistic regression, and k-nearest neighbor models to build our system. We had promising results with both models when using three machine learning models. We applied a hyper-parameter tuning technique throughout cross-validation to get a better model that does not face overfitting. We found that the decision tree model performs well with a coefficient of determination(R2) of 95.3% while the K-Nearest Neighbor model and logistic regression perform respectively with R2=93.15% and R2=89.78%. We also study the correlation between the predicted results and the expected results. We found that the prediction results of the decision tree model and the K-Nearest Neighbor model are correlated to the expected data, which proves the efficacy of the model.

Agricultural Decision System based on Advanced Machine Learning Models for Yield Prediction: Case of East African Countries

Abstract: Food security has become a real challenge for some organizations in charge of the food program and for the majority of countries, especially African countries. The United Nations Organizations’ has recently defined the end of hunger and the improvement of food security in 2030 as its primary goal. Improving food security could also pass through the handling of agricultural yield. Agricultural yield is affected by climate changes since this latest decade. Climate change is considered one of the major threats to agricultural development in Africa. Decision-making level and farmers need efficient analytical tools to help them in decision making. Machine learning has become an impressive predictive analytical tool for large volume of data. It has been used in many domains such as medicine, finance, sport, and recently in agriculture. In this work, we propose three crop prediction models : Crop Random Forest, Crop Gradient Boosting Machine and Crop Support Vector Machine. We combine climate data, crop production data, and pesticides data to develop a decision system based on advanced machine learning models. Despite the poor availability of data related to agriculture in Africa, we were able to propose a decision system able to predict the crop yield at the country level in fourteen East African countries. Our experimental results show that the three proposed machine learning models fit well the crop data with a high accuracy R 2 . The Root Mean Square Error ( R M S E ) and Mean Absolute Percentage Error ( M A P E ) associated to our models are very minimal because the agricultural prediction values are very close to reality. Our proposed models are reliable and generalize well the agricultural predictions in East Africa.