Farm-based Evaluation of Sustainable Alternative Irrigation Practices

Oad, S. J.; Hadiqa, Maqsood,; Qureshi, Abdul Latif; Ahmed, Shoaib; Channa, I. A.; Ali, Mahtab

doi:10.48084/etasr.2810

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Figure 3 can show simply the flowchart of TISD program. The data for environmental conditions are; the type of soil [24][25][26][27][28][29], topography of the land area [30][31], location and type of water source, quantity and quality of irrigation water [32][33][34], and climate area [35][36][37]. The data for economic conditions comprise; price of land [38], rates of nominal and real interest [12,39], cost of type of energy source [40][41][42][43], factor of energy inflation, costs of labor, factor of labor inflation, costs of all components of the trickle system used.…”

Section: Methodsmentioning

confidence: 99%

Assessment of environmental and economic impacts of trickle irrigation system

Khalifa

2022

IOP Conf. Ser.: Earth Environ. Sci.

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The systems of trickle irrigation offer the potential influential irrigation to increase crop yields and have been proven reasonable according to the engineering and agricultural opinion particularly in barren areas. Trickle irrigation systems that have been offered comparatively lately in Arab countries have demonstrated to save huge amounts of water and enhance crop yield. The computational program (Trickle Irrigation System Design, TISD) connected with the economic procedures were used to research the environmental and economic conditions of the agriculture system. The conditions of environment comprised the type of soil, the topography of land, the climate areas, the water quantum and quality, and the dimensions of land. The conditions of economic included the interest rates of nominal and real, the principal price of land, and the rates of labor and energy escalation. The research deemed the ratio of B/C indicating the effects of environmental and economic parameters applying the system of trickle irrigation. The research presented the crop rotation of tomato-sesame as a line-source, and the point-source was represented by citrus trees. The research results revealed a significant impact on the ratio of B/C for the parameters; soil type, land topography, and water quality. The other parameters evidenced a variety impacts among considerable, small, and negligible on the B/C ratio. The study represents a worthy perception for institutional considerations in agricultural economics.

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Section: Methodsmentioning

confidence: 99%

Assessment of environmental and economic impacts of trickle irrigation system

Khalifa

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Weather conditions were favorable for most of the crop's life cycle, and growers anticipated record production [2]. Agriculture is the foundation of all civilizations, but the emphasis is on increasing production without considering the environmental impacts that have emerged due to environmental degradation [3][4]. Plant diseases are extremely important because they can significantly affect both the quality and quantity of agricultural plants.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence Framework for Disease Detection in Potato Plants

Abbas,

Maqsood,

Ur Rehman

et al. 2024

Eng. Technol. Appl. Sci. Res.

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Agricultural products are a fundamental necessity for every country. When plants are afflicted with diseases, it influences the country's agricultural productivity, as well as its economic resources. Diseases are an important problem for potato plants, causing potatoes to be rejected and resulting in financial losses. Viruses and diseases in potatoes and field plants can be missed with the naked eye, particularly in the early stages of cultivation. The use of modern instruments and technology at an early stage of disease diagnosis dramatically reduces costs. This study used deep learning techniques to categorize and detect plant leaf diseases in photos taken from the Plant Village dataset. The dataset consists of 20,636 photos of plants and their diseases. This study focused on potato plants because it is the most common type of plant in the world, particularly in Pakistan. Convolutional Neural Network (CNN) methods were used to categorize plant leaf diseases into 15 classes, including three classes for healthy leaves and classes for several plant diseases such as fungal and bacterial infections, among others. The proposed models were trained and tested, achieving 98.29 and 98.029% accuracy, respectively.

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