Classification of Macronutrient Deficiencies in Maize Plant Using Machine Learning

Detection of nutritional deficiencies in plants is vital for improving crop productivity. Timely identification of nutrient deficiency through visual symptoms in the plants can help farmers take quick corrective action by appropriate nutrient management strategies. The application of computer vision and machine learning techniques offers new prospects in non-destructive field-based analysis for nutrient deficiency. Color and shape are important parameters in feature extraction. In this work, two different techniques are used for image segmentation and feature extraction to generate two different feature sets from the same image sets. These are then used for classification using different machine learning techniques. The experimental results are analyzed and compared in terms of classification accuracy to find the best algorithm for the two feature sets

REVIEW ON DETECTION OF RICE PLANT LEAVES DISEASES USING DATA AUGMENTATION AND TRANSFER LEARNING TECHNIQUES

The most important cereal crop in the world is rice (Oryza sativa). Over half of the world's population uses it as a staple food and energy source. Abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, and viruses, among others, impact the yield production and quality of rice grain. Farmers spend a lot of time and money managing diseases, and they do so using a bankrupt "eye" method that leads to unsanitary farming practices. The development of agricultural technology is greatly conducive to the automatic detection of pathogenic organisms in the leaves of rice plants. Several deep learning algorithms are discussed, and processors for computer vision problems such as image classification, object segmentation, and image analysis are discussed. The paper showed many methods for detecting, characterizing, estimating, and using diseases in a range of crops. The methods of increasing the number of images in the data set were shown. Two methods were presented, the first is traditional reinforcement methods, and the second is generative adversarial networks. And many of the advantages have been demonstrated in the research paper for the work that has been done in the field of deep learning

Digital image processing techniques for detecting, quantifying and classifying plant diseases.

Abstract. This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research

TECHNICAL information on highland rice in the states of Mato Grosso and Rondônia for the 2009-10 and 2010-11 crops seasons.

The Rice Technical Committee from those states CTA MT-RO has been involved with the enhancement of grain quality attributes and plant characteristics as well. The 3rd meeting of this Committee took place in 2009, from August 4 to 5, in Rondonópolis, under the coordination of the Rondonópolis and Southern Mato Grosso Food Industry Association (SIAR-SUL) with the support of many local institutions: Brazilian Supporting Service for Small Business (SEABRE, MT); Mato Grosso Research, Assistance and Rural Extension Association (EMPAER, MT); Mato Grosso Research Foundation (FAPEMAT), Paranatinga Mayor´s Office; Mato Grosso Rice Industry Association (SINDARROZ, MT); Department of Trade, Mining and Power (SICME); Caceres Food Industry Association (SIA); Mato Grosso Food Industry Inter-County Association (SIAMT). In addition, there was financial support from Rice Tec, farming support from Cabeça Branca Sementes de Arroz, Produtos Rei, Agroleste and Famato. Two important documents resulted from this meeting: the proceedings and a technical bulletin aggregating updated technical information for upland rice production in both states in the 2009/10 and 2010/11 cropping seasons.bitstream/CNPAF-2010/29877/1/doc-251.pd

Fertilizer Application on Crop Yield

This book is a printed edition of the Special Issue Fertilizer Application on Crop Yield that was published in Agronom

Irrigation Systems and Practices in Challenging Environments

The book Irrigation Systems and Practices in Challenging Environments is divided into two interesting sections, with the first section titled Agricultural Water Productivity in Stressed Environments, which consists of nine chapters technically crafted by experts in their own right in their fields of expertise. Topics range from effects of irrigation on the physiology of plants, deficit irrigation practices and the genetic manipulation, to creating drought tolerant variety and a host of interesting topics to cater for the those interested in the plant water soil atmosphere relationships and agronomic practices relevant in many challenging environments, more so with the onslaught of global warming, climate change and the accompanying agro-meteorological impacts. The second section, with eight chapters, deals with systems of irrigation practices around the world, covering different climate zones apart from showing casing practices for sustainable irrigation practices and more efficient ways of conveying irrigation waters - the life blood of agriculture, undoubtedly the most important sector in the world

Outputs: Potassium Losses from Agricultural Systems

Potassium (K) outputs comprise removals in harvested crops and losses via a number of pathways. No specific environmental issues arise from K losses to the wider environment, and so they have received little attention. Nevertheless, K is very soluble and so can be leached to depth or to surface waters. Also, because K is bound to clays and organic materials, and adsorbed K is mostly associated with fine soil particles, it can be eroded with particulate material in runoff water and by strong winds. It can also be lost when crop residues are burned in the open. Losses represent a potential economic cost to farmers and reduce soil nutritional status for plant growth. The pathways of loss and their relative importance can be related to: (a) the general characteristics of the agricultural ecosystem (tropical or temperate regions, cropping or grazing, tillage management, interactions with other nutrients such as nitrogen); (b) the specific characteristics of the agricultural ecosystem such as soil mineralogy, texture, initial soil K status, sources of K applied (organic, inorganic), and rates and timing of fertilizer applications. This chapter provides an overview of the main factors affecting K removals in crops and losses through runoff, leaching, erosion, and open burning

Influence of Potassium Fertilizer Application Timing on Cotton Production as Related to Soil Potassium on U.S. Coastal Plain Soils

Understanding soil K dynamics is highly significant in cotton production owing to its prominent role in cotton fiber quality. About 31 % of cotton production in the U.S. is concentrated in SE states, with coastal plain soils having low innate K availability. Crop fertilizer-K recommendations are primarily made worldwide and across the U.S. using pre-plant STK concentrations. A literature review on cotton K studies suggests that fertilizer-K recommendations based on pre-plant STK concentrations alone need fine-tuning to meet the increasing K demands in modern cultivars, variations in crop K requirement patterns, and varied soil K supplying capacity. Studies have been conducted to find the optimum fertilizer-K rate and split fertilizer-K application impact in cotton. However, there is a lack of studies assessing if whole K application at high nutrient requirement growth stages can improve K-use efficiency compared to the application at planting. Field studies were conducted at Edisto REC in Blackville, SC, from 2018 to 2021 on soils with varying STK concentrations to evaluate the impact of different fertilizer-K application timing on cotton growth, yield, fiber quality and K-use efficiency. Treatments included fertilizer-K application rates (KR) of 0, 46, 92, and 138 kg K ha-1 applied either at planting, first square growth stage, or first flower growth stage. In the 2018 to 2020 field studies, pre-plant STK concentrations were in the low category (16-30 mg kg-1 M-1 K), but a yield response was noted only in the 2020 study, with the highest yield recorded with KR of 92 kg K ha-1 applied at the time of cotton planting. In the 2018 and 2019 trials with low STK, the utilization of subsoil K in the clayey subsoil horizon (Bt) could have caused no yield response to the K application. Cotton growth was highest, with a 92 kg ha-1 rate, when applied at planting with no impact on lint percentage. The yield and fiber quality parameters showed similar trends across five years for parameters like fiber length, length uniformity, elongation, and contrasting trends concerning micronaire. However, fiber quality was reduced due to late planting and exposure to unfavorable weather conditions. Agronomic K-use efficiency increased two-fold with the single whole application at planting compared to the first square and flower stage application. Continued research on the impact of varying fertilizer-K rates and application timing in different soil types across South Carolina can give more insight into the soil-plant K dynamics existing in the region to further validate the fertilizer-K recommendations adopted in the state

Comparison of DRIS and critical level approach for evaluating nutrition status of wheat in District Hyderabad, Pakistan

Intensive cropping systems, improper use of fertilizers or no fertilizer application, and unreliable and poor quality of irrigation water have led to declining soil fertility in the district of Hyderabad, Pakistan. To date, no comprehensive overview exists on the nutrient status of the soils in the region and on yield constraints due to mineral nutrient deficiencies or imbalances. The objectives of this study were to a) determine the nutritional factors limiting productivity of wheat in Hyderabad, Pakistan, b) compare the DRIS and critical level approach for evaluating nutrient deficiencies in wheat, and c) identify the most limiting nutrient(s) that should be applied to increase and stabilize the yield of wheat in the region. Farmers’ fields (181) were selected on the basis of a survey of wheat-growing areas. Low- and high-yielding areas for two vegetation seasons (2007-08 and 2008-09) with different crop production management were selected. Plant sampling of wheat was done for shoot material at GS-29 (tillering stage) and for leaf tissue at GS-39 (emergence of flag leaf; Zadok et al. 1974). Sample analysis included the determination of macronutrients (N, P, K) and micronutrients (Zn, Cu, Fe, Mn, and B). For yield estimates at harvest, data on grains per spike, number of spikelets and thousand-grain weight were collected. Yield data were divided into low-and high-yielding populations according to the Cate and Nelson (1971) procedure for partitioning. The mean value and coefficient of variance were calculated for DRIS norms. The selected DRIS norms were: N/P, N/K, N/Cu, N/Fe, N/Mn, N/Zn, N/B, P/K, P/Cu, P/Fe, P/Mn, P/Zn, P/B, K/Cu, K/Fe, K/Mn, K/Zn, K/B, Cu/Fe, Cu/Mn, Cu/Zn, Cu/B, Fe/Mn, Fe/Zn, Fe/B, Mn/Zn, Mn/B and Zn/B. The DRIS indices were established for N, P, K, Zn, Fe, Cu, and B of the low- and high-yielding populations. These indices were compared with the developed norms. The interpretation of the wheat nutrients was done using the nutritional balance index (NBI) approach. The average index value for N at GS-39 is +47.04, which indicates that N is sufficient in the leaf tissue. The values of P and K are 58.49 and 21.67, respectively. These positive values show that N, P and K are sufficient in the leaf tissue, and that the wheat plants do not require any additional fertilization. The micronutrients are almost in good balance, i.e., neither sufficient nor deficient except for Fe. The index value of Fe is -93, whereas the index values for Mn, Zn, Cu, and B are -21.88, +17.77, -1.38 and -28.68, respectively. The NBI is 56.45, which shows that there is no high nutritional imbalance. The interpretation of the data with respect to deficiency and adequate and moderate supply shows that all nutrients are adequately supplied with exception of Zn and B, which show high values. However, the average index values of the shoot material at GS-29 are different; here the value for N is -54.27, which indicates that N is deficient at this growth stage. The same applies to P and K. The micronutrient values, however, are positive. This indicates that these nutrients are not in short supply compared to the macronutrients. The NBI is 75.23, which shows some nutritional imbalance. According the results of the critical level approach, the average concentration of N in the leaf tissue at GS-39 is deficient (which is in contrast to the DRIS evaluation), whereas all other nutrients are adequate. The average nutrient concentration in the shoot material at GS-29 is similar to that in the leaf at GS 39. The DRIS calculation also indicates that N is in short supply at the early growth stage, whereas Mn and B are present in high concentrations. Statistically, the values in the critical level approach are non-significant compared to those of the DRIS. However, DRIS has an advantage, as the DRIS norms for the study area are similar at both studied growth stages, which makes the interpretation easier. It has to be stressed, though, that the DRIS only indicates nutrient supply in relation to the other nutrients. According to the DRIS evaluation, if the N supply is improved during early development, other nutrients may become yield limiting, e.g., P and K. The DRIS norms can provide guidelines for the policy makers in the region regarding recommendations for appropriate fertilizer application.Vergleich von DRIS und der Methode der kritischen Konzentration für die Bewertung des Nährstoffstatus von Weizen im Distrikt Hyderabad, Pakistan Intensive Anbausysteme, falscher bzw. kein Einsatz von Düngemitteln sowie unzuverlässige Bereitstellung und schlechte Qualität des für die Bewässerung eingesetzten Wassers haben zu einer abnehmenden Bodenfruchtbarkeit im Distrikt Hyderabad, Pakistan, geführt. Es gibt keinen umfassenden Überblick über den Nährstoffstatus der Böden in der Region oder über ertragsbegrenzenden Mangel oder Nährstoffungleichgewicht. Die Ziele dieser Studie waren a) die Nährstoff-Faktoren zu bestimmen, welche die Weizenproduktivität in Hyderabad, Pakistan, begrenzen, b) die DRIS Methode bzw. die „critical nutrient level Methode“ („CNL“) (kritische Konzentration) für die Bewertung von Nährstoffmangel bei Weizen zu vergleichen, und c) die am meisten begrenzenden Nährstoffe zu bestimmen, welche die Weizenerträge in der Region am meisten limitieren. 181 Praxisschläge wurden für eine Bestandsaufnahme in Weizenanbaugebieten ausgewählt. Die Schläge wurden unterteilt in Hoch- bzw. Niedrigertragsflächen für zwei Anbauperioden (2007-08 und 2008-09). Sprossproben wurden zum Wachstumsstadium GS-29 (Bestockung) und vom Blattgewebe zum Wachstumsstadium GS-39 (Erscheinen des Fahnenblatts; nach Zadoks et al. 1975) genommen. Die Proben wurden auf Makronährstoffe (N, P, K) und Mikronährstoffe (Zn, Cu, Fe, Mn, B) analysiert. Für die Ertragsbestimmung wurden zur Ernte die Anzahl an Körnern pro Ähre, die Anzahl der Ährchen und das 1000-Körnergewicht bestimmt. Die Ertragsdaten wurden vwerendet, um die Gesamtpopulation in Hochertrags- bzw. Niedrigertragspopulationen zu unterteilen nach der Methode von Cate und Nelson (1971). Mittelwert und Variationskoeffizient wurden für die DRIS-Normen berechnet. Die ausgewählten DRIS Normen waren: N/P, N/K, N/Cu, N/Fe, N/Mn, N/Zn, N/B, P/K, P/Cu, P/Fe, P/Mn, P/Zn, P/B, K/Cu, K/Fe, K/Mn, K/Zn, K/B, Cu/Fe, Cu/Mn, Cu/Zn, Cu/B, Fe/Mn, Fe/Zn, Fe/B, Mn/Zn, Mn/B und Zn/B. Die DRIS-Indizes wurden für N, P, K, Zn, Fe, Cu, und B für beide Populationen berechnet und mit den DRIS-Normen verglichen. Die Interpretation der Weizennährstoffe wurde auf der Grundlage des nutritional balance index (NBI) durchgeführt. Der durchschnittliche Index für N bei GS-39 ist +47.04; dies deutet daraufhin, dass N im Blattgewebe zu diesem Zeitpunkt ausreichend vorhanden ist. Die P- bzw. K-Werte betrugen 58.49 bzw. 21.67. Daraus folgt, dass N, P und K im Blattgewebe ausreichend vorhanden sind, und dass die Weizenpflanzen keine zusätzlichen Düngemittelgaben benötigen. Die Mikronährstoffe sind fast ausgewogen vorhandenmit Ausnahme von Fe. Der Index für Fe ist -93, während die Werte Mn, Zn, Cu, bzw. B -21.88, +17.77, -1.38 bzw. -28.68 betragen. Der NBI beträgt 56.45; dies weist auf eine noch weitgehend ausgewogene Nährstoffversorgung zum Zeitpunkt der Bestockunghin. Die Interpretation der Daten nach der CNL Methode weist auf hohe Zn und B Werte hin. Jedoch unterscheiden sich die durchschnittlichen Indizes zum zweiten Probenahmezeitpunkt; hier beträgt der N-Wert -54.27, das heißt, dass N in diesem Wachstumsstadium nicht mehr ausreichend vorhanden ist, ebenso P und K. Die Werte der Mikronährstoffe sind jedoch positiv. Dies deutet darauf hin, dass diese Nährstoffe im Verhältnis zur Makronährstoffversorgung ausreichend vorhanden sind. Der NBI beträgt 75.23, was auf ein Nährstoffungleichgewicht hindeutet. Die Ergebnisse der CNL Analyse ergaben (im Gegensatz zur DRIS Evaluierung), dass die durchschnittliche N-Konzentration im Blattgewebe zum Zeitpunkt GS-39 nicht ausreichend war, während alle anderen Nährstoffe ausreichend vorhanden sind. Die durchschnittliche Nährstoffkonzentration im Spross bei GS-29 ähnelt der des Blattgewebes zum Zeitpunkt GS-39. Die DRIS-Berechnung zeigt außerdem, dass N zum diesem Zeitpunkt nicht ausreichend vorhanden ist, während die Mn- und B-Konzentrationen hoch sind. Statistisch gesehen, sind die CNL Werte verglichen mit den DRIS-Werten nicht signifikant voneinander unterschieden. Jedoch hat der DRIS-Ansatz den Vorteil, dass die DRIS-Normen im zu beiden untersuchten Wachstumsstadien ähnlich sind. Dies erleichtert die Interpretation. Jedoch muss betont werden, dass das DRIS lediglich die Nährstoffversorgung im Verhältnis zu anderen Nährstoffen aufzeigt. Auf der Grundlage der DRIS-Auswertungen kann angenommen werden, dass die Verbesserung der N-Versorgungzu GS-39 zu einer Limitierung des Ertrags durch andere Nährstoffe führen könnte, wie zum Beispiel P und K. Die DRIS-Normen können Richtlinien für die politischen Entscheidungsträger in der Region hinsichtlich sachgerechter Düngung zur Verfügung stellen

Encyclopedia of subsistence farming solutions : SAKpedia, 2018 Edition

This work was carried out with the aid of a grant from Canada’s International Development Research Centre (IDRC), and with financial support from the Government of Canada, provided through Global Affairs Canada (GAC)This encyclopedia for subsistence farming techniques is a “living” online, free, open-access book and searchable database that will be continuously updated and expanded. It serves as an in-depth resource for low-cost solutions to farming problems world wide. For instance, the availability of cheap fertilizers through collection and redistribution of human urine and animal manure can become a solution to restoring crop fertility through composting. Hundreds of other how-to’s of low cost applications for land and soil health, irrigation, seedling generation, crop pests, breeding, and simple post harvest systems are included