Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Emerging thermal imaging techniques for seed quality evaluation: Principles and applications

Due to the massive progress occurred in the past few decades in imaging, electronics and computer science, infrared thermal imaging technique has witnessed numerous technological advancement and smart applications in non-destructive testing and quality monitoring of different agro-food produces. Thermal imaging offers a potential non-contact imaging modality for the determination of various quality traits based on the infrared radiation emitted from target foods. The technique has been moved from just an exploration method in engineering and astronomy into an effective tool in many fields for forming unambiguous images called thermograms eventuated from the temperature and thermal properties of the target objects. It depends principally on converting the invisible infrared radiation emitted by the objects into visible two-dimensional temperature data without making a direct contact with the examined objects. This method has been widely used for different applications in agriculture and food science and technology with special applications in seed quality assessment. This article provides an overview of thermal imaging theory, briefly describes the fundamentals of the system and explores the recent advances and research works conducted in quality evaluation of different sorts of seeds. The article comprehensively reviewed research efforts of using thermal imaging systems in seed applications including estimation of seed viability, detection of fungal growth and insect infections, detection of seed damage and impurities, seed classification and variety identification.info:eu-repo/semantics/acceptedVersio

Decision Support System Data for Farmer Decision Making

The capacity of farmers and agricultural scientists to be able to make in-season decisions is dependent on accurate climate, soil and plant data. This paper will provide a review of the types of environmental and crop data that can be collected by sensors which can used for decision support systems (DSS) or be further interrogated for real time data mining and analysis. This paper also presents a review of the data requirements for agricultural decision making by firstly reviewing decision support frameworks and agricultural DSSs, data acquisition, sensors for data acquisition and examples of data incorporation for agricultural DSSs

An Efficient Deep Learning-based approach for Recognizing Agricultural Pests in the Wild

One of the biggest challenges that the farmers go through is to fight insect pests during agricultural product yields. The problem can be solved easily and avoid economic losses by taking timely preventive measures. This requires identifying insect pests in an easy and effective manner. Most of the insect species have similarities between them. Without proper help from the agriculturist academician it is very challenging for the farmers to identify the crop pests accurately. To address this issue we have done extensive experiments considering different methods to find out the best method among all. This paper presents a detailed overview of the experiments done on mainly a robust dataset named IP102 including transfer learning with finetuning, attention mechanism and custom architecture. Some example from another dataset D0 is also shown to show robustness of our experimented techniques

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Deterministic model approaches in identifying and quantifying technological challenges in rice production and research and in predicting population, rice production and consumption in Malaysia

In general, rice production and sufficiency is the main concern to all Asian countries in currently facing the ever growing population and climatic uncertainties. The consumption in Malaysia relies largely on the locally produced (70%) and imported (30%) rice for years. The price hike of this staple food, which can be categorized as a security food crop with an annual production of 1.6 million tons (beras) yielded from about 650,000 ha of the harvested paddy irrigated- and non-irrigated growing areas nationwide, could possibly be expensive to the lower-income consumers. With “no further reduction” in the modelled per capita rice consumption (82.3 kg/person/ year) versus the increasing population, various efforts must be made in term of research and technological advancement, increased cropping hectarage, as well as active extension program to increase the production of rice for consumption, self-sufficiency and more importantly, for having strong rice stock-file accumulation. Based on the data gathered from the past 27-years (1980 – 2007), the deterministic mathematical models of the Malaysian population, rice per capita consumption and five rice yield models versus years (1980 – 2007 and 2008 – 2030) were developed and predicted. The proposed model was based on the national average yields over the years and the model could be used to predict the yield ‘close’ to the nation’s rice production in the years ahead. The data on the crop cutting test or survey were used for comparison purposes. With the derivatives of the yield models, the quantitative technological advancement indexes were used in identifying the research objective, scope and areas, as well as in quantifying the contribution of crops and their management-related technologies in the past, present and predicted technological performances in rice production. To reach sufficient rice production at a relatively faster rate, the scope of the research’s objective should be based on the high yield model, in which the averaged yield could reach 13.4 t/ha in the year 2030. The priority order of the research areas would be irrigation/water > crop establishment-related management > sustainability of the existing management technology > large plot production-related adaptive studies (technological uniformity studies) > continual varietal improvement. The local released varieties are ecologically suited to the Malaysian rice growing areas, where varietal development and improvement are generally time consuming. With the current planted hectareage, coupled with the inclusion of the planned additional 100,000 ha (assumed to be staggered), as planned by the Ministry of Agriculture and with the conversion of the non-fully to fully irrigated areas by 2012, the Malaysian rice self-sufficiency is predicted to be observed/achieved in 2012. The ‘modified higher-order polynomial’ yield model which was conditioned with the scope of the above research objective and the area priorities predicts the rice production of 2.0, 4.4 and 9.1 million t/ha in 2010, 2020 and 2030, respectively. With the modelled minimum per capita consumption (82.3 g/person/year) and the predicted population of 29.3 (2010), 36.7 (2020) and 45.7 million (2030), the respective consumption, surplus and self-sufficiency would be 2.4, 3.0 and 3.8 million tons, -0.4, 1.3 and 5.3 million tons and 83, 144 and 241%, respectively. The surplus could then be used for the stock-pile accumulation and export

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production